Integer overflow in Series.astype('datetime64[D]')
#### Code Sample, a copy-pastable example if possible

```
>>> s = pd.Series(['9/22/1677', '4/11/2262'], dtype="U")

>>> s
0    9/22/1677
1    4/11/2262
dtype: object


>>> s.astype('datetime64')
0   1677-09-22
1   2262-04-11
dtype: datetime64[ns]


>>> s.astype('datetime64[s]')
0   1677-09-22
1   2262-04-11
dtype: datetime64[ns]


>>> s.astype('datetime64[m]')
0   1677-09-22
1   2262-04-11
dtype: datetime64[ns]


>>> s.astype('datetime64[h]')
0   1677-09-22
1   2262-04-11
dtype: datetime64[ns]


>>> s.astype('datetime64[D]')
0   2262-04-11    # <<<< wait, what?
1   2262-04-11
dtype: datetime64[ns]
```
#### Problem description

It appears that `astype('datetime64[D]')` breaks on the earliest representable full day -- it wraps around to the latest representable date.

While I know this is super edge case-y and highly unlikely to be worth a lot of time, the fact that it wraps around is indicative of a potential for integer overflow somewhere that may or may not bite us in the future.

#### Expected Output

`.astype('datetime64[D]')` should give 1677-09-22 instead of 2262-04-11.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.0
numpy            : 1.17.0
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.2
setuptools       : 41.1.0
Cython           : 0.29.13
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : None
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.14.1
pytables         : None
s3fs             : 0.3.3
scipy            : None
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

</details>
