text: Title: BUG: `.convert_dtypes(dtype_backend="pyarrow")` strips timezone from tz-aware pyarrow timestamp Series
  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.
  
  - [x] 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
  >>> s = pd.Series(pd.to_datetime(range(5), utc=True, unit="h"), dtype="timestamp[ns, tz=UTC][pyarrow]")
  >>> s
  0    1970-01-01 00:00:00+00:00
  1    1970-01-01 01:00:00+00:00
  2    1970-01-01 02:00:00+00:00
  3    1970-01-01 03:00:00+00:00
  4    1970-01-01 04:00:00+00:00
  dtype: timestamp[ns, tz=UTC][pyarrow]
  >>> s.convert_dtypes(dtype_backend="pyarrow")
  0    1970-01-01 00:00:00
  1    1970-01-01 01:00:00
  2    1970-01-01 02:00:00
  3    1970-01-01 03:00:00
  4    1970-01-01 04:00:00
  dtype: timestamp[ns][pyarrow]
  ```
  
  
  ### Issue Description
  
  Calling `.convert_dtypes(dtype_backend="pyarrow")` on a Series that is already a timezone aware pyarrow timestamp dtype strips the timezone information.
  
  Testing on older versions, this seems to be a regression introduced sometime between versions 2.0.3 and 2.1.0rc0
  
  ### Expected Behavior
  
  No change should be made to the dtype
  
  ### Installed Versions
  
  <details>
  
  INSTALLED VERSIONS
  ------------------
  commit                : 3f7bc81ae6839803ecc0da073fe83e9194759550
  python                : 3.12.2
  python-bits           : 64
  OS                    : Windows
  OS-release            : 10
  Version               : 10.0.19045
  machine               : AMD64
  processor             : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
  byteorder             : little
  LC_ALL                : None
  LANG                  : en_US.UTF-8
  LOCALE                : English_United States.1252
  
  pandas                : 3.0.0.dev0+1654.g3f7bc81ae6
  numpy                 : 2.1.3
  dateutil              : 2.9.0.post0
  pip                   : 24.3.1
  Cython                : None
  sphinx                : None
  IPython               : None
  adbc-driver-postgresql: None
  adbc-driver-sqlite    : None
  bs4                   : None
  blosc                 : None
  bottleneck            : None
  fastparquet           : None
  fsspec                : None
  html5lib              : None
  hypothesis            : None
  gcsfs                 : None
  jinja2                : None
  lxml.etree            : None
  matplotlib            : None
  numba                 : None
  numexpr               : None
  odfpy                 : None
  openpyxl              : None
  psycopg2              : None
  pymysql               : None
  pyarrow               : 18.0.0
  pyreadstat            : None
  pytest                : None
  python-calamine       : None
  pytz                  : None
  pyxlsb                : None
  s3fs                  : None
  scipy                 : None
  sqlalchemy            : None
  tables                : None
  tabulate              : None
  xarray                : None
  xlrd                  : None
  xlsxwriter            : None
  zstandard             : None
  tzdata                : 2024.2
  qtpy                  : None
  pyqt5                 : None
  
  </details>
  