BUG: pd.DataFrame is created inconsistently from numpy.ndarray for non-native pandas dtypes such as: datetime64[ms], etc..
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```python
import pandas as pd
import numpy as np

a = np.array(
    [
        ["2015-01-01", "2015-01-02", "2015-01-03"],
        ["2017-01-01", "2017-01-02", "2017-02-03"],
    ],
    dtype="datetime64[ms]",
)

print(pd.DataFrame(a))
           0          1          2
0 2015-01-01 2015-01-03 2017-01-02
1 2015-01-02 2017-01-01 2017-02-03


b = np.array(
    [
        ["2015-01-01", "2015-01-02", "2015-01-03"],
        ["2017-01-01", "2017-01-02", "2017-02-03"],
    ],
    dtype="datetime64[M]",
)

print(pd.DataFrame(b))
           0          1          2
0 2015-01-01 2015-01-03 2017-01-02
1 2015-01-02 2017-01-01 2017-02-03

c = np.array(
    [
        ["2015-01-01", "2015-01-02", "2015-01-03"],
        ["2017-01-01", "2017-01-02", "2017-02-03"],
    ],
    dtype="datetime64[ns]",
)

print(pd.DataFrame(c))
           0          1          2
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03

```

#### Problem description
The exists an inconsistency how pd.DataFrame() is created from 2D numpy.ndarray for not pandas native type. The dataframe should be transposed for datetime64[M], datetime64[ms] etc.


#### Expected Output
```python
print(pd.DataFrame(a))
           0          1          2
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03

print(pd.DataFrame(b))
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03
```

#### Possible Hint
```python

d = np.array(
    [
        ["2015-01-01", "2015-01-02", "2015-01-03"],
        ["2017-01-01", "2017-01-02", "2017-02-03"],
    ],
    dtype="datetime64[ms]",
    order="f",
)

print(pd.DataFrame(d))
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03

```

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

<details>

INSTALLED VERSIONS
------------------
commit           : db08276bc116c438d3fdee492026f8223584c477
python           : 3.8.5.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United Kingdom.1252

pandas           : 1.1.3
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 21.0
setuptools       : 49.6.0.post20210108
Cython           : None
pytest           : 6.2.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.18.1
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : 1.3.22
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
