BUG: dt.tz_convert() on categorical[datetime] resets index
- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

### Code Sample

```python
import itertools
import pandas as pd

ts_list = pd.date_range(start='2021-01-01T02:00:00', periods=3, freq='1D').to_list()
df = pd.DataFrame(data=list(itertools.islice(itertools.cycle(ts_list), 9)),
                  index=[2, 4, 5, 6, 7, 8, 11, 14, 15],
                  columns=['pure_datetime'])
df['categorical_datetime'] = df['pure_datetime'].astype(pd.CategoricalDtype())

"""
>>> df
         pure_datetime categorical_datetime
2  2021-01-01 02:00:00  2021-01-01 02:00:00
4  2021-01-02 02:00:00  2021-01-02 02:00:00
5  2021-01-03 02:00:00  2021-01-03 02:00:00
6  2021-01-01 02:00:00  2021-01-01 02:00:00
7  2021-01-02 02:00:00  2021-01-02 02:00:00
8  2021-01-03 02:00:00  2021-01-03 02:00:00
11 2021-01-01 02:00:00  2021-01-01 02:00:00
14 2021-01-02 02:00:00  2021-01-02 02:00:00
15 2021-01-03 02:00:00  2021-01-03 02:00:00

>>> df.dtypes
pure_datetime           datetime64[ns]
categorical_datetime          category
dtype: object
"""

# Now apply some function from .dt namespace.
"""
>>> df['pure_datetime'].dt.tz_localize('Europe/Berlin')
2    2021-01-01 02:00:00+01:00
4    2021-01-02 02:00:00+01:00
5    2021-01-03 02:00:00+01:00
6    2021-01-01 02:00:00+01:00
7    2021-01-02 02:00:00+01:00
8    2021-01-03 02:00:00+01:00
11   2021-01-01 02:00:00+01:00
14   2021-01-02 02:00:00+01:00
15   2021-01-03 02:00:00+01:00
Name: pure_datetime, dtype: datetime64[ns, Europe/Berlin]
"""
# All is fine with pure datetime column

# BUT, when applied to categorical column, the index is reset:
"""
>>> df['categorical_datetime'].dt.tz_localize('Europe/Berlin')
0   2021-01-01 02:00:00+01:00
1   2021-01-02 02:00:00+01:00
2   2021-01-03 02:00:00+01:00
3   2021-01-01 02:00:00+01:00
4   2021-01-02 02:00:00+01:00
5   2021-01-03 02:00:00+01:00
6   2021-01-01 02:00:00+01:00
7   2021-01-02 02:00:00+01:00
8   2021-01-03 02:00:00+01:00
Name: categorical_datetime, dtype: datetime64[ns, Europe/Berlin]
"""

# Naive construction of new columns yields convoluted results
df['localized_pure'] = df['pure_datetime'].dt.tz_localize('Europe/Berlin')
df['localized_categorical'] = df['categorical_datetime'].dt.tz_localize('Europe/Berlin')
"""
>>> df
         pure_datetime categorical_datetime            localized_pure     localized_categorical
2  2021-01-01 02:00:00  2021-01-01 02:00:00 2021-01-01 02:00:00+01:00 2021-01-03 02:00:00+01:00
4  2021-01-02 02:00:00  2021-01-02 02:00:00 2021-01-02 02:00:00+01:00 2021-01-02 02:00:00+01:00
5  2021-01-03 02:00:00  2021-01-03 02:00:00 2021-01-03 02:00:00+01:00 2021-01-03 02:00:00+01:00
6  2021-01-01 02:00:00  2021-01-01 02:00:00 2021-01-01 02:00:00+01:00 2021-01-01 02:00:00+01:00
7  2021-01-02 02:00:00  2021-01-02 02:00:00 2021-01-02 02:00:00+01:00 2021-01-02 02:00:00+01:00
8  2021-01-03 02:00:00  2021-01-03 02:00:00 2021-01-03 02:00:00+01:00 2021-01-03 02:00:00+01:00
11 2021-01-01 02:00:00  2021-01-01 02:00:00 2021-01-01 02:00:00+01:00                       NaT
14 2021-01-02 02:00:00  2021-01-02 02:00:00 2021-01-02 02:00:00+01:00                       NaT
15 2021-01-03 02:00:00  2021-01-03 02:00:00 2021-01-03 02:00:00+01:00                       NaT
"""

```

#### Problem description

When working on categorical columns, I would expect all methods, that can be called without error or warning, to yield the same results as when applied to the pure (category-expanded) columns. Some methods from the `.dt`-namespace (at least `tz_localize()` and `tz_convert()`) seem to return non-consistent results.
In particular, they reset the index of the input column (a Series in fact).

#### Expected Output

I expect the method `.dt.tz_localize()` to not reset the index when applied to a Series of datetime categoricals.


#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python           : 3.9.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.14393
machine          : AMD64
processor        : Intel64 Family 6 Model 85 Stepping 0, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : de_DE.cp1252

pandas           : 1.3.2
numpy            : 1.21.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 57.4.0
Cython           : None
pytest           : 6.2.4
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
