clib.converison._to_numpy: Add tests for pandas.Series with datetime dtypes

[seisman]

This PR adds tests for pandas.Series with datetime dtypes. Address #3600.

In pandas, datetime dtypes can be specified in following ways:

1. Via NumPy dtypes, e.g., "datetime64[s]"
2. Via pandas.DatetimeTZDtype, e.g., pd.DatetimeTZDtype(s, tz="UTC") or "datetime64[s, UTC]"
3. Via pyarrow timestamp types, e.g., pd.ArrowDtype(pyarrow.Timestamp(s, tz="UTC")) or "timestamp[s, UTC][pyarrow]"

---

The following codes help us understand the default conversion behaviors:

>>> import numpy as np
>>> import pandas as pd

>>> # The sample data
>>> data = [pd.Timestamp("2024-01-02T03:04:05"), pd.Timestamp("2024-01-02T03:04:06")]

---

Via NumPy dtypes. The conversions are done as expected.

>>> series = pd.Series(data, dtype="datetime64[s]")
>>> series
0   2024-01-02 03:04:05
1   2024-01-02 03:04:06
dtype: datetime64[s]
>>> np.ascontiguousarray(series)
array(['2024-01-02T03:04:05', '2024-01-02T03:04:06'],
      dtype='datetime64[s]')

---

Via pd.DateTimeTZDtype with TZ. The pandas.series object is converted to object dtype. So we need to deal with the conversion manually. The expected numpy dtype and TZ information can be accessed via series.dtype.base and series.dtype.tz.

>>> series = pd.Series(data, dtype="datetime64[s, America/New_York]")
>>> series
0   2024-01-02 03:04:05-05:00
1   2024-01-02 03:04:06-05:00
dtype: datetime64[s, America/New_York]
>>> np.ascontiguousarray(series)
array([Timestamp('2024-01-02 03:04:05-0500', tz='America/New_York'),
       Timestamp('2024-01-02 03:04:06-0500', tz='America/New_York')],
      dtype=object)

>>> series.dtype.base
dtype('<M8[s]')
>>> series.dtype.tz
<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>

In pandas 2.0, there was a bug (pandas-dev/pandas#52705) that pd.DateTimeTZDtype with any units are stored with dtype in ns resolution. The bug was fixed in pandas 2.1 (pandas-dev/pandas#52706), but there is no workaround on our side so the related tests are marked as xfail for pandas 2.0

---

Via pa.Timestamp. The pandas.Series object is converted to object dtype. So, we need to deal with it manually. The expected numpy datetime type and TZ information can be accessed via series.dtype.numpy_dtype and series.dtype.pyarrow_dtype.tz.

>>> series = pd.Series(data, dtype="timestamp[s, America/New_York][pyarrow]")
>>> series
0    2024-01-01 22:04:05-05:00
1    2024-01-01 22:04:06-05:00
dtype: timestamp[s, tz=America/New_York][pyarrow]

>>> np.ascontiguousarray(series)
array([Timestamp('2024-01-01 22:04:05-0500', tz='America/New_York'),
       Timestamp('2024-01-01 22:04:06-0500', tz='America/New_York')],
      dtype=object)
>>> series.dtype.numpy_dtype
dtype('<M8[s]')
>>> series.dtype.pyarrow_dtype.tz
'America/New_York'

In pandas 2.0, series.dtype.numpy_dtype is dtype('O'), and it doesn't have the series.dt.tz_convert method.