text: Title: BUG: value_counts() returns error/wrong result with PyArrow categorical columns with nulls
  Body: ### Pandas version checks
  
  - [X] I have checked that this issue has not already been reported.
  
  - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
  
  - [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
  
  
  ### Reproducible Example
  
  ```python
  import pandas as pd
  import pyarrow as pa
  
  # First case: just one column. It gives the error below
  pd.DataFrame( { 'A': [ 'a1', pd.NA ] }, dtype = pd.ArrowDtype( pa.dictionary( pa.int32(), pa.utf8() ) ) ).value_counts( dropna = False )
  
  # Second case: more than one column. It gives the wrong result below
  pd.concat( [
      pd.DataFrame( { 'A': [ 'a1', 'a2' ], 'B': [ 'b1', pd.NA ] }, dtype = pd.ArrowDtype( pa.string() ) ),
      pd.DataFrame( { 'C': [ 'c1', 'c2' ], 'D': [ 'd1', pd.NA ] }, dtype = pd.ArrowDtype( pa.dictionary( pa.int32(), pa.utf8() ) ) )
  ], axis = 1 ).value_counts( dropna = False )
  ```
  
  
  ### Issue Description
  
  ### First Case
  It gives the following error:
  
  ```python-traceback
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  Cell In[6], line 1
  ----> 1 pd.DataFrame( { 'A': [ 'a1', pd.NA ] }, dtype = pd.ArrowDtype( pa.dictionary( pa.int32(), pa.utf8() ) ) ).value_counts( dropna = False )
  
  File C:\Python\Lib\site-packages\pandas\core\frame.py:7519, in DataFrame.value_counts(self, subset, normalize, sort, ascending, dropna)
     7517 # Force MultiIndex for a list_like subset with a single column
     7518 if is_list_like(subset) and len(subset) == 1:  # type: ignore[arg-type]
  -> 7519     counts.index = MultiIndex.from_arrays(
     7520         [counts.index], names=[counts.index.name]
     7521     )
     7523 return counts
  
  File C:\Python\Lib\site-packages\pandas\core\indexes\multi.py:533, in MultiIndex.from_arrays(cls, arrays, sortorder, names)
      530     if len(arrays[i]) != len(arrays[i - 1]):
      531         raise ValueError("all arrays must be same length")
  --> 533 codes, levels = factorize_from_iterables(arrays)
      534 if names is lib.no_default:
      535     names = [getattr(arr, "name", None) for arr in arrays]
  
  File C:\Python\Lib\site-packages\pandas\core\arrays\categorical.py:3069, in factorize_from_iterables(iterables)
     3065 if len(iterables) == 0:
     3066     # For consistency, it should return two empty lists.
     3067     return [], []
  -> 3069 codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
     3070 return list(codes), list(categories)
  
  File C:\Python\Lib\site-packages\pandas\core\arrays\categorical.py:3069, in <genexpr>(.0)
     3065 if len(iterables) == 0:
     3066     # For consistency, it should return two empty lists.
     3067     return [], []
  -> 3069 codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
     3070 return list(codes), list(categories)
  
  File C:\Python\Lib\site-packages\pandas\core\arrays\categorical.py:3042, in factorize_from_iterable(values)
     3037     codes = values.codes
     3038 else:
     3039     # The value of ordered is irrelevant since we don't use cat as such,
     3040     # but only the resulting categories, the order of which is independent
     3041     # from ordered. Set ordered to False as default. See GH #15457
  -> 3042     cat = Categorical(values, ordered=False)
     3043     categories = cat.categories
     3044     codes = cat.codes
  
  File C:\Python\Lib\site-packages\pandas\core\arrays\categorical.py:451, in Categorical.__init__(self, values, categories, ordered, dtype, fastpath, copy)
      447 if dtype.categories is None:
      448     if isinstance(values.dtype, ArrowDtype) and issubclass(
      449         values.dtype.type, CategoricalDtypeType
      450     ):
  --> 451         arr = values._pa_array.combine_chunks()
      452         categories = arr.dictionary.to_pandas(types_mapper=ArrowDtype)
      453         codes = arr.indices.to_numpy()
  
  AttributeError: 'Index' object has no attribute '_pa_array'
  ```
  Indeed, the same error is returned also if no `pd.NA` is present.
  
  ### Second case
  It gives the following result:
  
  ```python
  A   B     C   D 
  a1  b1    c1  d1    1
  a2  <NA>  c2  d1    1
  Name: count, dtype: int64
  ```
  
  **Note that in second line D is d1 and not `<NA>`.**
  
  A more complete example in this JupyterLab notebook: [value_counts() Bug.pdf](https://github.com/user-attachments/files/18133225/value_counts.Bug.pdf)
  
  
  ### Expected Behavior
  
  The expected behavior is analogous to the result obtained with the NumPy backend.
  
  ### First case
  
  ```python
  A  
  a1     1
  <NA>    1
  Name: count, dtype: int64
  ```
  
  ### Second case
  
  ```python
  A   B     C   D 
  a1  b1    c1  d1    1
  a2  <NA>  c2  <NA>    1
  Name: count, dtype: int64
  ```
  
  ### Installed Versions
  
  <details>
  
  INSTALLED VERSIONS
  ------------------
  commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
  python                : 3.12.8
  python-bits           : 64
  OS                    : Windows
  OS-release            : 2019Server
  Version               : 10.0.17763
  machine               : AMD64
  processor             : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
  byteorder             : little
  LC_ALL                : None
  LANG                  : None
  LOCALE                : English_United States.1252
  
  pandas                : 2.2.3
  numpy                 : 2.1.2
  pytz                  : 2024.2
  dateutil              : 2.9.0.post0
  pip                   : 24.3.1
  Cython                : None
  sphinx                : None
  IPython               : 8.29.0
  adbc-driver-postgresql: None
  adbc-driver-sqlite    : None
  bs4                   : 4.12.3
  blosc                 : None
  bottleneck            : 1.4.2
  dataframe-api-compat  : None
  fastparquet           : None
  fsspec                : None
  html5lib              : None
  hypothesis            : None
  gcsfs                 : None
  jinja2                : 3.1.4
  lxml.etree            : 5.3.0
  matplotlib            : 3.9.2
  numba                 : None
  numexpr               : 2.10.1
  odfpy                 : None
  openpyxl              : 3.1.5
  pandas_gbq            : None
  psycopg2              : None
  pymysql               : None
  pyarrow               : 18.1.0
  pyreadstat            : None
  pytest                : None
  python-calamine       : None
  pyxlsb                : None
  s3fs                  : None
  scipy                 : None
  sqlalchemy            : None
  tables                : None
  tabulate              : 0.9.0
  xarray                : None
  xlrd                  : None
  xlsxwriter            : None
  zstandard             : 0.23.0
  tzdata                : 2024.2
  qtpy                  : None
  pyqt5                 : None
  
  </details>
  