BUG: groupby.min has a side effect on groupby.apply
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
import pandas as pd

df = pd.DataFrame(
    {
        "col1": [0, 1, 2, 3],
        "col4": [17, 13, 16, 15],
        "col5": [-4, -5, -6, -7],
    }
)
by=["col4", "col5"]
apply_function = min

gb = df.groupby(by, as_index=True)

df1 = gb.apply(apply_function)
print(df1)

df2 = gb.min()
print(df2)

df3 = gb.apply(apply_function)
print(df3)
```

#### Problem description

[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]

In the code above two calls to `gb.apply(apply_function)` produce different output. The reason for this is that `groupby.min` is called before 2nd `apply` and makes its output different and incorrect.

#### Expected Output

Expected that both calls to `gb.apply(apply_function)` produce the same output.

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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.0-26-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.4
numpy            : 1.18.4
pytz             : 2019.2
dateutil         : 2.7.3
pip              : 20.1.1
setuptools       : 47.1.0
Cython           : 0.29.17
pytest           : 5.4.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : 0.4.1
xlsxwriter       : None
lxml.etree       : 4.5.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.12.0
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.1
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : 0.13.2
pyarrow          : 0.16.0
pytables         : None
pytest           : 5.4.2
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.4.1
sqlalchemy       : 1.3.17
tables           : 3.6.1
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.46.0
</details>
