BUG: Series.clip() behaviour is wrong with Series[datetime] comparison to pd.Timestamp.{min,max}
- [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, a copy-pastable example

```python
import pandas as pd
from datetime import datetime
df = pd.DataFrame({"OOB": [datetime(1,1,1), datetime(9999,9,9)]})
```

#### Actual output

```
>>> df["OOB"].clip(lower=pd.Timestamp.min, upper=pd.Timestamp.max)
0    2262-04-11 23:47:16.854775807
1    1677-09-21 00:12:43.145224193
Name: OOB, dtype: object
```

#### Problem description

The problem is that the `datetime(1,1,1)` ended up being clipped to `pd.Timestamp.max` i.e the lowest value became the highest and vice-versa.

I suspect that this is to do with the interaction between datetime objects and Timestamps. The last version where this was functioning as I expected was `1.2.5`.

Notes:
- I couldn't use the equivalent datetime to pd.Timestamp.{min,max} as the lower and upper bound because even then I would get an OOB error when trying to `pd.to_timestamp` the `Series`. Obviously there are kind of gross ways to end up with a `Series` that has been clipped to min/max.

#### Expected Output

This is what I expected to happen (and what happened in previous versions.
```
>>> df["OOB"].clip(lower=pd.Timestamp.min, upper=pd.Timestamp.max)
0    1677-09-21 00:12:43.145224193
1    2262-04-11 23:47:16.854775807
Name: OOB, dtype: object
```

Here is what happens if I clip using datetimes...

```
>>> df["OOB"].clip(lower=pd.Timestamp.min.to_pydatetime(), upper=pd.Timestamp.max.to_pydatetime())
sys:1: UserWarning: Discarding nonzero nanoseconds in conversion
0    1677-09-21 00:12:43.145224
1    2262-04-11 23:47:16.854775
```

I get the expected output this way however, I fail in my ultimate goal of obtaining a clipped `Series[Timestamp]` and just get a `Series[datetime]`.

```
>>> pd.to_datetime(df["OOB"].clip(lower=pd.Timestamp.min.to_pydatetime(), upper=pd.Timestamp.max.to_pydatetime()))
...
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1677-09-21 00:12:43
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python           : 3.8.10.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.1.0
Version          : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_AU.UTF-8
LOCALE           : en_AU.UTF-8

pandas           : 1.3.1
numpy            : 1.20.3
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.2.1
setuptools       : 49.6.0.post20210108
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : 3.4.3
blosc            : None
feather          : None
xlsxwriter       : 1.4.3
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 3.0.1
IPython          : 7.24.1
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.2
fsspec           : 0.8.7
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.2
numexpr          : 2.7.3
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 4.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.3
sqlalchemy       : 1.4.17
tables           : None
tabulate         : 0.8.9
xarray           : 0.18.2
xlrd             : None
xlwt             : None
numba            : 0.53.1

</details>
