[Python] Difference in timezone-awareness of result when calling to_pandas between unnested and nested timestamp arrays

### Describe the bug, including details regarding any error messages, version, and platform.

When you call `.to_pandas()` on a timestamp array, you get timezone-aware values. When you call `.to_pandas()` on a nested timestamp array, you get timezone-naive values. For example:

```python
import pandas as pd
import pyarrow as pa

ts = pandas.Timestamp('2024-01-01 12:00:00+0000', tz = 'Europe/Paris')

# unnested, we get a timezone-aware result
pa.Array.from_pandas([myts]).to_pandas()[0]
# => Timestamp('2024-01-01 13:00:00+0100', tz='Europe/Paris')

# nested, we get a timezone-naive result
pa.Array.from_pandas([[myts]]).to_pandas()[0][0]
# => numpy.datetime64('2024-01-01T12:00:00.000000')
```

While the values appear correct (which is good), the unnested case is timezone-aware while the nested case is timezone-naive. This difference may be surprising to users and would require extra steps on their part to re-construct a timezone-aware result if that was their goal.

Another difference I notice in the above output is that the unnested version is returned as a pandas `Timestamp` while the nested version is returned as numpy `datetime64`. It's my understanding that numpy's datetimes aren't timezone-aware ([ref](https://numpy.org/devdocs/reference/arrays.datetime.html)) so it seems possible PyArrow is inheriting that behavior. The pandas docs [point to the arrays.DatetimeArray extensiontype](https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/arrays.html#datetime-data) which I don't think PyArrow is making use of.

Is it possible to have a consistent result with respect to timezone-awareness in this case?

### Component(s)

Python