BUG: apply on Series of dtype category produces dtype category when float was expected
I'm trying to get the left edge from a Series of Intervals. Unexpectedly, the result is a Series of dtype category. I'm also getting a FutureWarning.

```python
>>> import pandas as pd
>>> s = pd.Series(pd.cut([1, 2, 3], 2))
>>> s
0    (0.998, 2.0]
1    (0.998, 2.0]
2      (2.0, 3.0]
dtype: category
Categories (2, interval[float64]): [(0.998, 2.0] < (2.0, 3.0]]
>>> s.apply(lambda x: x.left)
C:\Users\My.Name\.virtualenvs\MyRepo-nagZr6A2\lib\site-packages\pandas\io\formats\format.py:1403: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this wi
ll return a view on self.
  for val, m in zip(values.ravel(), mask.ravel())
0    0.998
1    0.998
2    2.000
dtype: category
Categories (2, float64): [0.998 < 2.000]
```

Interestingly, the individual cells have objects with the correct dtype:

```python
>>> result = s.apply(lambda x: x.left)
>>> type(result[0])
<class 'numpy.float64'>
```

However, operations you would expect from a Series of floats to work do not work:

```python
>>> result + 1
[...]
TypeError: unsupported operand type(s) for +: 'Categorical' and 'int'
```

<details><summary>pd.show_versions()</summary>
INSTALLED VERSIONS<br>
------------------<br>
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa<br>
python           : 3.8.7.final.0  <br>
python-bits      : 64  <br>
OS               : Windows  <br>
OS-release       : 10<br>
Version          : 10.0.19041<br>
machine          : AMD64<br>
processor        : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD<br>
byteorder        : little<br>
LC_ALL           : None<br>
LANG             : None<br>
LOCALE           : de_DE.cp1252<br>
pandas           : 1.2.1<br>
numpy            : 1.20.0<br>
pytz             : 2021.1<br>
dateutil         : 2.8.1<br>
pip              : 20.3.3<br>
setuptools       : 51.1.2<br>
Cython           : None<br>
pytest           : 6.2.2<br>
hypothesis       : None<br>
sphinx           : None<br>
blosc            : None<br>
feather          : None<br>
xlsxwriter       : None<br>
lxml.etree       : None<br>
html5lib         : None<br>
pymysql          : None<br>
psycopg2         : None<br>
jinja2           : 2.11.3<br>
IPython          : 7.20.0<br>
pandas_datareader: None<br>
bs4              : None<br>
bottleneck       : None<br>
fsspec           : None<br>
fastparquet      : None<br>
gcsfs            : None<br>
matplotlib       : None<br>
numexpr          : None<br>
odfpy            : None<br>
openpyxl         : 3.0.6<br>
pandas_gbq       : None<br>
pyarrow          : None<br>
pyxlsb           : None<br>
s3fs             : None<br>
scipy            : 1.6.0<br>
sqlalchemy       : 1.3.23<br>
tables           : None<br>
tabulate         : 0.8.7<br>
xarray           : None<br>
xlrd             : None<br>
xlwt             : None<br>
numba        : None<br>
</details>
