BUG: to_csv produces improper output for categorical datetimes
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```python
>>> ser = pd.to_datetime(pd.Series(["2021-03-27"]), format="%Y-%m-%d")
>>> ser
0   2021-03-27
dtype: datetime64[ns]
>>> ser.to_csv()
',0\n0,2021-03-27\n'
>>> ser.astype("category")
0   2021-03-27
dtype: category
Categories (1, datetime64[ns]): [2021-03-27]
>>> ser.astype("category").to_csv()
',0\n0,1616803200000000000\n'
>>> ser.astype("category").to_csv(date_format="%Y-%m-%dT%H:%M:%S")
',0\n0,1616803200000000000\n'
```

#### Problem description

When a categorical datetime is written as CSV, it disregards date_format (even if given explicitly) and writes the timestamp in integer nanoseconds.

#### Expected Output

```python
>>> ser.astype("category").to_csv()
',0\n0,2021-03-27\n'
>>> ser.astype("category").to_csv(date_format="%Y-%m-%dT%H:%M:%S")
',0\n0,2021-03-27T00:00:00\n'
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.9.1.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.11.8-arch1-1
Version          : #1 SMP PREEMPT Sun, 21 Mar 2021 01:55:51 +0000
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           :
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.19.5
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.2.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 7.20.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.7
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
