text: Title: [BUG] Unable to use multilevel='raw_values' parameter in error metric when benchmarking.
  Body: **Describe the bug**
  <!--
  A clear and concise description of what the bug is.
  -->
  When benchmarking, you have to specify the error metric(s) to use. Setting `multilevel='raw_values'` in the metric object results in error.
  
  **To Reproduce**
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  The code below is copied from the tutorial found here. https://www.sktime.net/en/latest/examples/04_benchmarking_v2.html
  
  The only change from the tutorial is with cell [5].
  
  ```python
  # %% [1]
  from sktime.benchmarking.forecasting import ForecastingBenchmark
  from sktime.datasets import load_airline
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.performance_metrics.forecasting import MeanSquaredPercentageError
  from sktime.split import ExpandingWindowSplitter
  
  # %% [2]
  benchmark = ForecastingBenchmark()
  
  # %% [3]
  benchmark.add_estimator(
      estimator=NaiveForecaster(strategy="mean", sp=12),
      estimator_id="NaiveForecaster-mean-v1",
  )
  benchmark.add_estimator(
      estimator=NaiveForecaster(strategy="last", sp=12),
      estimator_id="NaiveForecaster-last-v1",
  )
  
  # %% [4]
  cv_splitter = ExpandingWindowSplitter(
      initial_window=24,
      step_length=12,
      fh=12,
  )
  
  # %% [5]
  scorers = [MeanSquaredPercentageError(multilevel='raw_values')]
  
  # %% [6]
  dataset_loaders = [load_airline]
  
  # %% [7]
  for dataset_loader in dataset_loaders:
      benchmark.add_task(
          dataset_loader,
          cv_splitter,
          scorers,
      )
      
  # %% [8]
  results_df = benchmark.run("./forecasting_results.csv")
  
  ```
  
  **Expected behavior**
  <!--
  A clear and concise description of what you expected to happen.
  -->
  `results_df = benchmark.run("./forecasting_results.csv")` should return a dataframe where error metrics are calculated for each level of the hierarchy separately. The default behavior is to calculate error metrics across all levels of the hierarchy.
  
  **Additional context**
  <!--
  Add any other context about the problem here.
  -->
  
  Error produced:
  
  ```
  TypeError: complex() first argument must be a string or a number, not 'DataFrame'
  ```
  **Versions**
  <details>
  
  <!--
  Please run the following code snippet and paste the output here:
  
  from sktime import show_versions; show_versions()
  -->
  System:
      python: 3.12.9 | packaged by conda-forge | (main, Feb 14 2025, 07:48:05) [MSC v.1942 64 bit (AMD64)]
     machine: Windows-10-10.0.19045-SP0
  
  Python dependencies:
            pip: 25.0
         sktime: 0.36.0
        sklearn: 1.6.1
         skbase: 0.12.0
          numpy: 2.0.1
          scipy: 1.15.1
         pandas: 2.2.3
     matplotlib: 3.10.0
         joblib: 1.4.2
          numba: None
    statsmodels: 0.14.4
       pmdarima: 2.0.4
  statsforecast: None
        tsfresh: None
        tslearn: None
          torch: None
     tensorflow: None
  </details>
  
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