BUG: DataFrame.rolling(axis=1) operations drop/ignore float16 and float32 columns
- [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.

---

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

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

# Generate a 4x6 DataFrame
df = pd.DataFrame(np.arange(24).reshape(4, 6), columns=list("abcdef"))
# Make each column a different data type
df = df.astype({"a":"float16", "b":"float32", "c":"float64", "d":"int8", "e":"int16", "f":"int32"})

print(df)
# Output:
#      a     b     c   d   e   f
# 0   0.0   1.0   2.0   3   4   5
# 1   6.0   7.0   8.0   9  10  11
# 2  12.0  13.0  14.0  15  16  17
# 3  18.0  19.0  20.0  21  22  23
print(df.dtypes)
# Output:
# a    float16
# b    float32
# c    float64
# d       int8
# e      int16
# f      int32
# dtype: object

# Rolling minimum across rows
print(df.rolling(window=2, min_periods=1, axis=1).min())
# Output. Notice how the float16 and float32 columns were removed:
#       c     d     e     f
# 0   2.0   2.0   3.0   4.0
# 1   8.0   8.0   9.0  10.0
# 2  14.0  14.0  15.0  16.0
# 3  20.0  20.0  21.0  22.0
```

#### Problem description
It seems that rolling operations along rows (`axis=1`) incorrectly omit columns containing `float16`s and `float32`s. The same operations work as expected along columns (`axis=0`), however.

#### Expected Output
```python
# Convert float16 and float32 columns to float64s as a workaround
df = df.astype({"a":"float64", "b":"float64"})
# Rolling minimum across rows again
print(df.rolling(window=2, min_periods=1, axis=1).min())
# Output:
#       a     b     c     d     e     f
# 0   0.0   0.0   1.0   2.0   3.0   4.0
# 1   6.0   6.0   7.0   8.0   9.0  10.0
# 2  12.0  12.0  13.0  14.0  15.0  16.0
# 3  18.0  18.0  19.0  20.0  21.0  22.0
```

#### Possible Cause
A change made in #36458, [specifically this line](https://github.com/phofl/pandas/blob/fb85045056fc9cd773e714287a7a88ac55e19023/pandas/core/window/rolling.py#L250).
It seems that `"float"` is an alias specifically for `np.float64`, not `np.float32` or `np.float16`. Changing that line to
`obj = obj.select_dtypes(include="number", exclude=["timedelta"])` 
to include all numeric values seemed to fix the issue in this case. I can open a PR if there don't seem to be any issues with this.

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

<details>

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.9.1.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : English_Canada.1252

pandas           : 1.2.4
numpy            : 1.20.3
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.1.2
setuptools       : 57.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.6.3
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 7.24.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.2
numexpr          : None
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : 0.18.2
xlrd             : None
xlwt             : None
numba            : None

</details>
