pandas datetime type is not preserved on write & re-read when dates are stored within an array

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

What i expected:
via a `pandas.to_parquet()` i should be able to write to disk and preserve the type of my data

What happened
The type of my data changed

Long description
I'm storing an array of pandas timestamp objects within a column of a pandas dataframe. i'm writing this dataframe to disk and reading it back at a later date and concatenating to it. However, when i read the dataframe back in via `read_parquet`, the type of the timestamps in my array have changed from a pandas.Timestamp to a numpy.datetime64.

This makes it challenging to append to the dataframe on disk as I get this error:
```
pyarrow.lib.ArrowInvalid: ('numpy.datetime64 scalars cannot be mixed with other Python scalar values currently', 'Conversion failed for column with type object')
```

The error is happening on the write and read. here's a minimal example:

```
from datetime import datetime, date
import pandas as pd
print(pd.__version__)

df = pd.DataFrame([(1, "x", date.today(), [pd.to_datetime(datetime(2018, 1, 2, 18, 53))])],
columns=["number", "string", "date", "datetime"])
print(df)
print(type(df['datetime'][0][0]))
df.to_parquet("/tmp/test_datetime.parquet")
df2 = pd.read_parquet("/tmp/test_datetime.parquet")
print(df2)
print(type(df2['datetime'][0][0]))
concat_df = pd.concat([df,df2])
concat_df.to_parquet("/tmp/test_datetime_concat.parquet")
```

the output of this snippet is:

```
2.2.3
number string date datetime
0 1 x 2025-09-13 [2018-01-02 18:53:00]

number string date datetime
0 1 x 2025-09-13 [2018-01-02T18:53:00.000000]

```
with the associated error
```
{
"name": "ArrowInvalid",
"message": "('numpy.datetime64 scalars cannot be mixed with other Python scalar values currently', 'Conversion failed for column datetime with type object')",
}
```
the pandas version is `2.2.3` and the pyarrow version is `18.1.0`. I'm not certain which one it is happening in, but because it happened on write, i'm starting here.

### Component(s)

Python