BUG: select_dtypes considers numpy int and pandas nullable integer types as the same
- [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.
Version 1.2.3

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---


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

```python
import pandas as pd
df = pd.DataFrame([{"A": 1, "B": 2}, {"A": 3}]).astype({"B": "Int64"})

print(df.dtypes, end="\n\n")

print(df.select_dtypes("Int64"))  # Expect to select only column B
```

#### Actual output

```
A    int64
B    Int64
dtype: object

   A     B
0  1     2
1  3  <NA>
```

#### Problem description

Calling `select_dtypes("Int64")` (`Int` with capital "I") selects both `int64` and `Int64` columns.

Also applies to other int types, e.g. `int32` & `Int32`.

Similar behavior is also obtained with `df.select_dtypes(pd.Int64Dtype)`

#### Expected Output

```
A    int64
B    Int64
dtype: object

      B
0     2
1  <NA>
```

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

<details>

```
INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.8.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : French_Canada.1252

pandas           : 1.2.3
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : 6.2.2
hypothesis       : None
sphinx           : 3.5.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.21.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.3
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.51.2
```

</details>
