BUG: Nullable integer dtype leads to a fragmentation PerformanceWarning
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

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

df = pd.DataFrame(np.random.randint(0,100,size=(100, 100)), dtype='Int32')
df.reset_index()
```


### Issue Description

When using the new [nullable Integer](https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html) dtype, pandas seams to generate one [IntegerArray](https://pandas.pydata.org/docs/reference/api/pandas.arrays.IntegerArray.html) per columns.
This will inevitably leads to a fragmentation performance warning while using more than 100 columns.
```python
df = pd.DataFrame(np.random.randint(0,100,size=(100, 100)), dtype='Int32')
df.reset_index() # Warn
```
```
>>> PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index()
```

Of course this is due to an high number of fragments:
```python
df._data.nblocks
>>> 100
```


### Expected Behavior

Maybe we could have a proper nullable integer matrix representation

### Installed Versions

<details>
INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.9.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18363
machine          : AMD64
processor        : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.3.4
numpy            : 1.21.1
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 56.0.0
Cython           : None
pytest           : 6.2.4
hypothesis       : None
sphinx           : 4.1.2
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : 7.26.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 2021.07.0
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.7.1
sqlalchemy       : 1.4.22
tables           : None
tabulate         : None
xarray           : 0.19.0
xlrd             : None
xlwt             : None
numba            : None

</details>
