BUG: wrong df.groupby().groups when grouping with [Grouper(freq=), ...] (GH33132)
- [ ] closes #33132 
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

The proposed change breaks the equivalence of `df.groupby(grouper)` and `df.groupby([grouper])`, captured by test https://github.com/pandas-dev/pandas/blob/8415f9ba2f5f3df6b10975c49d0e3af257149e64/pandas/tests/groupby/test_timegrouper.py#L740
Currently executing this test results in: 
(**The correct behavior is the one in `result`, and not in `expected`.**)

```python

import pandas as pd
import pandas._testing as tm

data_frame = {
    "location": ["shanghai", "beijing", "shanghai"],
    "time": pd.Series(
        ["2017-08-09 13:32:23", "2017-08-11 23:23:15", "2017-08-11 22:23:15"],
        dtype="datetime64[ns]",
    ),
    "value": [1, 2, 3],
}
data_frame = pd.DataFrame(data_frame).set_index("time")
grouper = pd.Grouper(freq="D")

grouped = data_frame.groupby(grouper)
result = grouped.count()
grouped = data_frame.groupby([grouper])
expected = grouped.count()

print(result)
#             location  value
# time                       
# 2017-08-09         1      1
# 2017-08-10         0      0
# 2017-08-11         2      2
print(expected)
#             location  value
# time                       
# 2017-08-09         1      1
# 2017-08-11         2      2
```

The problem occurs when we have something like `df.groupby([pd.Grouper(freq=)])` (i.e., a list with one element of type `TimeGrouper`).

One way to fix this is by capturing the `keys` arg in https://github.com/pandas-dev/pandas/blob/8305a856c150126ef899544091395104ac6141e2/pandas/core/groupby/groupby.py#L356-L363 and, if it is a `[pd.Grouper]`, we transform it to a `pd.Grouper`.

Does this modification make sense? please merge master and see if you can resolve the issues. Closing this PR as stale, but feel free to ping if you'd like to continue working on it.