OneHotEncoder fails on missing values when Pandas uses PyArrow backend

### Describe the bug

A while back @thomasjpfan and @lorentzenchr contributed https://github.com/scikit-learn/scikit-learn/pull/17317 which enabled missing value support in `OneHotEncoder`
> For object dtypes, None and np.nan is support for missing values.

Pandas 2.0 now supports an Arrow backend which uses `pandas._libs.missing.NAType` instead of either of the currently supported options (`None` or `np.nan`) to represent its missing values. This causes `OneHotEncoder` to fail

### Steps/Code to Reproduce

```python
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBClassifier

pipe = Pipeline(
    [
        (
            "preprocess",
            ColumnTransformer(
                [
                    (
                        "categorical_features",
                        OneHotEncoder(),
                        ["category"],
                    )
                ]
            ),
        ),
        (
            "model",
            XGBClassifier(n_estimators=3)
        )
    ]
)

# Native Pandas types work
df_native = pd.DataFrame(
    {
        "category": ["a", "b", np.nan, "d"],
        "label": [0, 1, 0, 1],
    }
)
pipe.fit(df_native[["category"]], df_native["label"])

# Arrow types do not work
# TypeError: Encoders require their input argument must be uniformly strings or numbers. 
# Got ['NAType', 'str']
df_arrow = df_native.convert_dtypes(dtype_backend="pyarrow")
pipe.fit(df_arrow[["category"]], df_arrow["label"])

# On inspection, the null value has different representations
null_idx = 2
print(type(df_native["category"].iloc[null_idx]))  # <class 'float'>
print(type(df_arrow["category"].iloc[null_idx]))   # <class 'pandas._libs.missing.NAType'>
```

### Expected Results

Sklearn should work with the new Pandas Arrow backend

### Actual Results

```
Traceback (most recent call last):
  File "sklearn/utils/_encode.py", line 174, in _unique_python
    uniques = sorted(uniques_set)
              ^^^^^^^^^^^^^^^^^^^
  File "missing.pyx", line 419, in pandas._libs.missing.NAType.__bool__
TypeError: boolean value of NA is ambiguous

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/pipeline.py", line 423, in fit
    Xt = self._fit(X, y, **fit_params_steps)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/pipeline.py", line 377, in _fit
    X, fitted_transformer = fit_transform_one_cached(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "joblib/memory.py", line 353, in __call__
    return self.func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/pipeline.py", line 957, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/utils/_set_output.py", line 157, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/compose/_column_transformer.py", line 754, in fit_transform
    result = self._fit_transform(X, y, _fit_transform_one)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/compose/_column_transformer.py", line 681, in _fit_transform
    return Parallel(n_jobs=self.n_jobs)(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/utils/parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "joblib/parallel.py", line 1863, in __call__
    return output if self.return_generator else list(output)
                                                ^^^^^^^^^^^^
  File "joblib/parallel.py", line 1792, in _get_sequential_output
    res = func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/utils/parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/pipeline.py", line 957, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/utils/_set_output.py", line 157, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/base.py", line 919, in fit_transform
    return self.fit(X, y, **fit_params).transform(X)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/preprocessing/_encoders.py", line 985, in fit
    self._fit(
  File "sklearn/preprocessing/_encoders.py", line 98, in _fit
    result = _unique(Xi, return_counts=compute_counts)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "sklearn/utils/_encode.py", line 42, in _unique
    return _unique_python(
           ^^^^^^^^^^^^^^^
  File "sklearn/utils/_encode.py", line 179, in _unique_python
    raise TypeError(
TypeError: Encoders require their input argument must be uniformly strings or numbers. Got ['NAType', 'str']
```

### Versions

```shell
System:
    python: 3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:37:07) [Clang 15.0.7 ]
executable: /var/lib/conda/envs/lightning-2023-10/bin/python3.11
   machine: macOS-13.5.2-arm64-arm-64bit
Python dependencies:
      sklearn: 1.3.1
          pip: 23.3
   setuptools: 68.2.2
        numpy: 1.24.4
        scipy: 1.11.3
       Cython: None
       pandas: 2.1.1
   matplotlib: 3.8.0
       joblib: 1.3.2
threadpoolctl: 3.2.0
Built with OpenMP: True
threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: /private/var/lib/conda/envs/lightning-2023-10/lib/libopenblas.0.dylib
        version: 0.3.24
threading_layer: openmp
   architecture: VORTEX
       user_api: openmp
   internal_api: openmp
    num_threads: 10
         prefix: libomp
       filepath: /private/var/lib/conda/envs/lightning-2023-10/lib/libomp.dylib
        version: None
```

COMMENT:
I think this is still different because we were not using the pyarrow backend but the resulting dataframe should be similar.

Labels: Bug