BUG: dataframe sum with level drops timedelta 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, Minimal

The data:

```python
>>> d = pd.DataFrame(
    { "a": [pd.Timedelta(hours=6),pd.Timedelta(hours=7)]
    , "b": [12.1,13.3]
    })

>>> d
                a     b
0 0 days 06:00:00  12.1
1 0 days 07:00:00  13.3

>>> d.dtypes
a    timedelta64[ns]
b            float64
dtype: object
```

The operation with unexpected results:

```python
>>> d.sum(level=0)
      b
0  12.1
1  13.3
```

The expected results:

```python
>>> d.sum(level=0)
                a     b
0 0 days 06:00:00  12.1
1 0 days 07:00:00  13.3
```

#### Code Sample, Real-World

There's no point to summing a level without a multi-index. This example is more serious.


```python
>>> d = pd.DataFrame(
 { "a": [1,1]
 , "b": [2,2]
 , "c": [4,5]
 , "d": [pd.Timedelta(hours=6),pd.Timedelta(hours=7)]
 , "e": [12.1,13.3]
 })

>>> d = d.set_index(["a","b","c"])

>>> d
                    d     e
a b c
1 2 4 0 days 06:00:00  12.1
    5 0 days 07:00:00  13.3

>>> d.dtypes
d    timedelta64[ns]
e            float64
dtype: object
```

Unexpected results:

```python
>>> d.sum(level=["a","b"])
        e
a b
1 2  25.4
```

Expected results:

```python
>>> d.sum(level=["a","b"])
                  d     e
a b
1 2 0 days 13:00:00  25.4
```

#### Problem description

If it can be summed without the `level` argument it should be summed with the `level` argument. At the very least, raise an error rather than dropping the column.

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.8.final.0
python-bits      : 64
OS               : CYGWIN_NT-10.0-19041
OS-release       : 3.1.7-340.x86_64
Version          : 2020-08-22 17:48 UTC
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 41.2.0
Cython           : 0.29.22
pytest           : None
hypothesis       : None
sphinx           : None
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           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : 2.7.3
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
