REGR: ValueError: cannot convert float NaN to integer - on dataframe.reset_index() in pandas 1.1.0
- [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
from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar
from pandas.core.dtypes.missing import na_value_for_dtype
import numpy as np

dtype = np.datetime64().dtype
length = 10
works_value = np.NaN
fails_value = na_value_for_dtype(dtype)

# works
construct_1d_arraylike_from_scalar(works_value, length, dtype)

# fails
construct_1d_arraylike_from_scalar(fails_value, length, dtype)
```
Results in:
```
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    construct_1d_arraylike_from_scalar(value, length, dtype)
  File "/usr/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 1453, in construct_1d_arraylike_from_scalar
    subarr.fill(value)
ValueError: cannot convert float NaN to integer
```

#### Problem description

This is a problem was found when calling `reset_index` on a MultiIndex Dataframe with an index containing datetime information. The expected behaviour would be that it would return an array of NaTs, which ironically only happens if you pass it `np.NaN`.
I'm not familiar with the source code of numpy, so I can only speculate on how to fix it, but I would assume we would need special handling for time values, so we fill time arrays with `np.NaN`s instead of `pandas._libs.tslibs.nattype.NaTType`s.

#### Expected Output

`array(['NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT'], dtype=datetime64)`

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.7.9-1-MANJARO
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.0.5
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 49.2.0
Cython           : 0.29.21
pytest           : 5.4.3
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.2
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: 0.10.0dev0
bs4              : 4.9.1
bottleneck       : 1.3.2
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.2
matplotlib       : 3.3.0
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.3
pyxlsb           : None
s3fs             : None
scipy            : 1.5.1
sqlalchemy       : 1.3.17
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
