BUG: inconsistent `groupby.apply` behaviour depending on column dtypes
I see strange `groupby.apply` behaviour: resulting index depends on the existence of the `Timestamp` dtype column. MWE: 

```python
import pandas as pd
df = pd.DataFrame({
    'date_val': {
        0: pd.Timestamp('2017-01-01 00:00:00'),
        1: pd.Timestamp('2017-01-01 00:00:00'),
        2: pd.Timestamp('2017-02-01 00:00:00'),
        3: pd.Timestamp('2017-02-01 00:00:00')
    },
    'uid': {
        0: '1',
        1: '2',
        2: '1',
        3: '2'
    },
    'str_val': {
        0: str('2017-01-01 00:00:00'),
        1: str('2017-01-01 00:00:00'),
        2: str('2017-02-01 00:00:00'),
        3: str('2017-02-01 00:00:00')
    }
})

df1 = df.groupby('uid', as_index=False)[['uid', 'str_val', 'date_val']].apply(lambda x: x.sort_values(by='str_val',ascending=True))
df2 = df.groupby('uid', as_index=False)[['uid', 'str_val']].apply(lambda x: x.sort_values(by='str_val',ascending=True))
df3 = df.groupby('uid', as_index=False)[['uid', 'date_val']].apply(lambda x: x.sort_values(by='date_val',ascending=True))

print(df1)
print(df2)
print(df3)
```

Output:

```
  uid              str_val   date_val
0   1  2017-01-01 00:00:00 2017-01-01
1   2  2017-01-01 00:00:00 2017-01-01
2   1  2017-02-01 00:00:00 2017-02-01
3   2  2017-02-01 00:00:00 2017-02-01
    uid              str_val
0 0   1  2017-01-01 00:00:00
  2   1  2017-02-01 00:00:00
1 1   2  2017-01-01 00:00:00
  3   2  2017-02-01 00:00:00
  uid   date_val
0   1 2017-01-01
1   2 2017-01-01
2   1 2017-02-01
3   2 2017-02-01
```
I expect the index to be like in the second output (`df2`). Indexes in `df1` and `df3` seem strange.

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

<details>

INSTALLED VERSIONS
------------------
commit           : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python           : 3.8.0.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
Version          : Darwin Kernel Version 18.7.0: Mon Apr 27 20:09:39 PDT 2020; root:xnu-4903.278.35~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.UTF-8

pandas           : 1.3.2
numpy            : 1.21.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.2
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
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           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.7.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
