BUG: `Series.sum` & `Series.mean` are inconsitent for `timedelta64` dtype
- [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
>>> pd.__version__
'1.1.0'
>>> s = pd.Series([None, None], dtype='timedelta64[ns]')
>>> s
0   NaT
1   NaT
dtype: timedelta64[ns]
>>> s.sum()
Timedelta('0 days 00:00:00')
>>> s.mean()
NaT
>>> s.sum() / 2 # Mean
Timedelta('0 days 00:00:00')
```

#### Problem description

When there are `NaT` values in the series, the sum in the above case to returns `0`, which will result in incorrect/inconsistent calculations if other calculations rely on the result of `sum`, for instance if we try calculating a mean from the result of sum, we get `0` instead of correct result `NaT`(as returned by `Series.mean`).

#### Expected Output


When there are `NaT` values in the series, I'd expect the sum in the above case to return `NaT`, as `Series.mean` returns `NaT` as expected. 


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

<details>

INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Sun Jul  5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8
pandas           : 1.1.0
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 49.1.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
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
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
