BUG: Regression in version 1.3.0: outer/right merge converts Int64 column to object if side-specific values are present
- [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.

---

When merging data frames on an **Int64** column, the resulting dtype differs between pandas 1.2.5 and 1.3.0.

* If all values are present on the left and right side, the Int64 dtype is retained successfully both in 1.2.5 and 1.3.0, for all merge methods (inner, left, right, outer)
* If there are specific values on both sides, dtype is converted depending on merge method:
  * 1.2.5 keeps **Int64** for inner and left merges, converts to **int64** for outer and right merges
  * 1.3.0 keeps **Int64** for inner and left merges, converts to **object** for outer and right merges

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

#### ALL VALUES IDENTICAL: Correct behaviour

```python
# pandas 1.2.5
df1 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[9]: 
# nr    Int64
```

```python
# pandas 1.3.0
df1 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[9]: 
# nr    Int64
```

#### SIDE-SPECIFIC VALUES PRESENT: Differing behaviour, regression on 1.3.0

```python
# pandas 1.2.5
df1 = pd.DataFrame(dict(nr=[1,2,3  ])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[  2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[1]: 
# nr    int64
pd.merge(df1, df2, how="right", on="nr").dtypes
# Out[2]: 
# nr    int64
pd.merge(df1, df2, how="left", on="nr").dtypes
# Out[3]: 
# nr    Int64
pd.merge(df1, df2, how="inner", on="nr").dtypes
# Out[4]: 
# nr    Int64
```

```python
# pandas 1.3.0
df1 = pd.DataFrame(dict(nr=[1,2,3  ])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[  2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[1]: 
# nr    object
pd.merge(df1, df2, how="right", on="nr").dtypes
# Out[2]: 
# nr    object
pd.merge(df1, df2, how="left", on="nr").dtypes
# Out[3]: 
# nr    Int64
pd.merge(df1, df2, how="inner", on="nr").dtypes
# Out[4]: 
# nr    Int64
```

#### Problem description
When merging on an Int64 column, the Int64 dtype is lost in certain circumstances (outer and right joins, side-specific values present).
* On pandas 1.2.5, the column drops to **int64** (mostly equivalent to Int64, except for NULLs)
* On pandas 1.3.0, the column turns into an **object** (string) column (not equivalent to Int64 at all)

I found this when matches of a merged Int64 key against other Int64 columns silently stopped matching after the update to 1.3.0.
On 1.2.5, there was no problem, as int64 columns match correctly against Int64 ones.

#### Expected Output
* Ideally, the **Int64** dtype should be retained in all the above cases
* If this is currently not possible, the 1.2.5 behaviour of converting to **int64** is preferred, as the column still mostly works as expected
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : f00ed8f47020034e752baf0250483053340971b0
python           : 3.8.10.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19042
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : German_Switzerland.1252
pandas           : 1.3.0
numpy            : 1.20.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.1.3
setuptools       : 51.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : None
IPython          : 7.25.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 4.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.3
sqlalchemy       : 1.4.0b1
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
