REGR: outer merge resulting in missing datetime values converts to int
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

df = pd.DataFrame(
    {
        "x": [0, 1],
        "date": pd.to_datetime(["2020-01-05T12:00", "2020-01-05T13:00"]),
    }
)
df2 = pd.DataFrame({"x": [0, 2], "y": [0, 1]})

merged = pd.merge(df, df2, on="x", how="outer")
merged.info()
print(merged)
```

> ```
>     <class 'pandas.core.frame.DataFrame'>
>     Int64Index: 3 entries, 0 to 2
>     Data columns (total 3 columns):
>      #   Column  Non-Null Count  Dtype  
>     ---  ------  --------------  -----  
>      0   x       3 non-null      int64  
>      1   date    3 non-null      object 
>      2   y       2 non-null      float64
>     dtypes: float64(1), int64(1), object(1)
>     memory usage: 96.0+ bytes
> ```

> ```
>    x   date                 y
> 0  0   1578225600000000000  0.0
> 1  1   1578229200000000000  NaN
> 2  2  -9223372036854775808  1.0
> ```

```python
merged["date"].map(type)
```

> ```
>     0    <class 'int'>
>     1    <class 'int'>
>     2    <class 'int'>
>     Name: date, dtype: object
> ```

```python
print(pd.merge(df, df2, on="x", how="inner"))
```

> ```
>    x                date  y
> 0  0 2020-01-05 12:00:00  0
> ```

#### Problem description

Merging 2 DataFrames which result in missing Datetime values silently converts the column to integers with an `object` dtype, instead of keeping `datetime64[ns]` as dtype, which was the behaviour in pandas 0.25.3, and the behaviour I would expect.

#### Expected Output

```python
merged = pd.merge(df, df2, on="x", how="outer")
merged.info()
print(merged)
```

    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 3 entries, 0 to 2
    Data columns (total 3 columns):
    x       3 non-null int64
    date    2 non-null datetime64[ns]
    y       2 non-null float64
    dtypes: datetime64[ns](1), float64(1), int64(1)
    memory usage: 96.0 bytes
       x                date    y
    0  0 2020-01-05 12:00:00  0.0
    1  1 2020-01-05 13:00:00  NaN
    2  2                 NaT  1.0


```python
merged["date"].map(type)
```

> ```
>     0    <class 'pandas._libs.tslibs.timestamps.Timesta...
>     1    <class 'pandas._libs.tslibs.timestamps.Timesta...
>     2        <class 'pandas._libs.tslibs.nattype.NaTType'>
>     Name: date, dtype: object
> ```

This is the output from Python 0.25.3

Whether the `y`-column should be converted to float or `Int64` is open for debate, but not the issue at hand here.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 8.1
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.0rc0
numpy            : 1.17.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 45.1.0.post20200119
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.11.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 3.0.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
