BUG:OutOfBoundsDatetime on numpy timestamps, but not on datetime.datetime
- [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

```python
import datetime
import numpy as np
import pandas as pd
# This works fine:
pd.DataFrame([datetime.datetime(3015,1,1,0,0,0)])
# returned dtype is object, all good.

# But the same timestamp as a numpy object fails:
pd.DataFrame(np.array(['3015-01-01T00:00:00.000'], dtype='datetime64[ms]'))
# OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 3015-01-01 00:00:00
```

#### Problem description

pd.DataFrame tries to infer dtypes, and this works for datetime.datetime objects. If they are within the datetime64[ns] limits, they are converted, while if they are outside, the dtype is kept as `object`. For numpy datetime64 objects as input, the same flexibility is not in place, and pd.DataFrame() errors hard instead during auto-conversion.

#### Expected Output

dtype: object when pd.DataFrame is called on the numpy timestamps above

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

<details>

INSTALLED VERSIONS
------------------
commit           : 7d32926db8f7541c356066dcadabf854487738de
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-62-generic
Version          : #70-Ubuntu SMP Tue Jan 12 12:45:47 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.2
numpy            : 1.19.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 44.0.0
Cython           : None
pytest           : 6.2.2
hypothesis       : None
sphinx           : 3.4.3
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.6.2
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : None
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : 1.3.22
tables           : None
tabulate         : None
xarray           : 0.16.2
xlrd             : 1.2.0
xlwt             : None
numba            : None


</details>
