BUG: `get_dummies` works on a single integer value column, but fails silently when applied to multiple columns. 
- [x] I could not find this issue under open issues when searching: "bug get_dummies"

- [X] I have confirmed this bug exists on the latest version of pandas (1.2.0)

---

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

```python
# A sample dataframe. Last column has integer values.
df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['1', '2', '3'], 'C': [1, 2, 3]})

# Encoding the integer values fine. 
df2 = pd.get_dummies(df['C'], prefix='C')

# Encoding the string values works fine.:
df3 = pd.get_dummies(df[['A', 'B']], prefix=['a', 'b'])

# Encoding both this fails silently:
df4 = pd.get_dummies(df)

# Supplying prefixes makes the issue apparent:
df5 = pd.get_dummies(df, prefix=['A', 'B', 'C'])
```

#### Problem description
Pandas `get_dummies` will one-hot encode a single integer valued categorical column. **However, it silently ignores any further columns containing numeric categories.** 

The last line of the minimal working example (with prefix list argument) results in:
```
ValueError: Length of 'prefix' (3) did not match the length of the columns being encoded (2).
```

#### Why the current behavior a problem and why the expected output is a better solution:
The expected output matches what the documentation advertises. 

#### Expected Output
All columns passed to `get_dummies` should be considered categorical and encoded, including those containing integers.

**or**

No integer columns should be allowed. Produce a warning/error/update the docs. 

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

<details>

INSTALLED VERSIONS
------------------
commit           : 3e89b4c4b1580aa890023fc550774e63d499da25
python           : 3.8.5.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.2.0
numpy            : 1.19.2
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 51.1.2.post20210110
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
