BUG: `pd.to_numeric` has an inconsistent behavior for `datetime` objects
- [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.

- [x] (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
>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0    1629590400000000000
dtype: int64
>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN
dtype: float64
>>>
>>> pd.to_numeric(pd.NaT, errors="coerce")
nan
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0   -9223372036854775808
dtype: int64
>>> pd.Series([pd.NaT]).apply(partial(pd.to_numeric), errors="coerce")
0   NaN
dtype: float64

```

#### Problem description

When using `pd.to_numeric` to convert a `pd.Series` with dtype `datetime64[ns]`, it returns different values than converting the series *value by value*

#### Expected Output

Converting a `pd.Series` as a *whole* should be the same than converting it *value by value*.
I am not sure about what the correct output should be, but IMO the output should be consistent in these two scenarios. 

What I suggest:
- For no-null values, returns the same value. Maybe the integer?
- For `pd.NaT`, always returns `np.NaN`

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

I am using the latest version of `master` until today

<details>

INSTALLED VERSIONS
------------------
commit           : e39ea3024cebb4e7a7fd35972a44637de6c41650
python           : 3.8.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 1.4.0.dev0+517.gc3761e24d8
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 49.2.0.post20200714
Cython           : 0.29.21
pytest           : 5.4.3
hypothesis       : None
sphinx           : 3.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.2.9
lxml.etree       : 4.5.2
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : 0.7.4
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.4
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.0
sqlalchemy       : 1.3.18
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.50.1

</details>
