BUG: mask method fails on Timedelta series with numpy 1.20
- [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 numpy as np
import pandas as pd

td = pd.Series([pd.Timedelta(days=i) for i in range(3)])

try:
    # same as td.mask([False, False, True])
    td.where([False, False, True], np.nan)
except TypeError as err:
    print(err)
#> The DTypes <class 'numpy.dtype[float16]'> and <class 'numpy.dtype[timedelta64]'> do not have a common DType. For example they cannot be stored in a single array unless the dtype is `object`.

# gives same result as above on numpy < 1.20
print(td.where([False, False, True], pd.NaT))
#> 0      NaT
#> 1      NaT
#> 2   2 days
#> dtype: timedelta64[ns]

# also ok
print(td.where([False, False, True], 10.0))
#> 0               10.0
#> 1               10.0
#> 2    2 days 00:00:00
#> dtype: object

td.where([False, False, True], np.nan, inplace=True) # ok
print(td)
#> 0      NaT
#> 1      NaT
#> 2   2 days
#> dtype: timedelta64[ns]
```

<sup>Created on 2021-02-02 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>

#### Problem description

With numpy 1.20.0, `mask` on a Series of dtype timedelta64 raises a `TypeError`. The code above shows that the issue comes from the internals of `where` and happens only when a `np.nan` is passed as replacement value.

I noticed that `inplace=True` avoids the issue.

#### Expected Output

`td.mask(...)` should return the same as `where(..., pd.NaT)`

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

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.8.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.10.7-3-MANJARO
Version          : #1 SMP PREEMPT Fri Jan 15 21:11:34 UTC 2021
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.1
numpy            : 1.20.0
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 49.2.1
Cython           : None
pytest           : 6.2.2
hypothesis       : 6.1.1
sphinx           : 3.4.3
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
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>