BUG: Grouping by frequency doesn't preserve order within each group
- [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, a copy-pastable example

```python
import pandas as pd

A = pd.DataFrame(list(zip([
    '2018-08-30 18:01:37.774447+00:00',
    '2018-08-30 16:07:41.125230+00:00',
    '2019-08-30 13:08:16.467217+00:00',
    '2016-08-30 13:12:43.530383+00:00',
    '2018-08-30 12:13:26.028439+00:00'
], 
[
    '2018-05-30 18:01:37.774447+00:00',
    '2018-05-30 12:01:37.774447+00:00',
    '2018-05-30 11:01:37.774447+00:00',
    '2018-09-30 13:13:26.028439+00:00',
    '2018-09-30 12:13:26.028439+00:00'
], [1,1,1,2,2])), columns=['datetime', 'datetime2', 'grp'])
A['datetime'] = pd.to_datetime(A['datetime'], utc=True)
A['datetime2'] = pd.to_datetime(A['datetime2'], utc=True)

A = A.sort_values('datetime')

print(list(A.groupby('grp'))[0]) # this one is sorted correctly

# (1,
#                            datetime                        datetime2  grp
#  1 2018-08-30 16:07:41.125230+00:00 2018-05-30 12:01:37.774447+00:00    1
#  0 2018-08-30 18:01:37.774447+00:00 2018-05-30 18:01:37.774447+00:00    1
#  2 2019-08-30 13:08:16.467217+00:00 2018-05-30 11:01:37.774447+00:00    1)

print(list(A.groupby(pd.Grouper(key='datetime2', freq='1d')))[0]) # this one is not sorted

# (Timestamp('2018-05-30 00:00:00+0000', tz='UTC', freq='D'),
#                            datetime                        datetime2  grp
#  2 2019-08-30 13:08:16.467217+00:00 2018-05-30 11:01:37.774447+00:00    1
#  1 2018-08-30 16:07:41.125230+00:00 2018-05-30 12:01:37.774447+00:00    1
#  0 2018-08-30 18:01:37.774447+00:00 2018-05-30 18:01:37.774447+00:00    1)

```

#### Problem description

Pandas docs for groupby (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) says:

> Groupby preserves the order of rows within each group.

This works when grouping by column values, but when doing it by frequency this doesn't hold, as we can see in the example above.

Also, tests for this behaviour don't include dates: https://github.com/nickeubank/pandas/blob/3b49fad4558a6d4f3db1c1d360a7048d1b633211/pandas/tests/test_groupby.py#L5439

#### Expected Output
The groups should preserve order when grouping by frequency.


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

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.7.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-130-generic
Version          : #134-Ubuntu SMP Tue Jan 5 20:46:26 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.1
numpy            : 1.19.5
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 20.1.1
setuptools       : 47.1.1
Cython           : 0.29.21
pytest           : 6.1.0
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.5 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.15.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.5
fastparquet      : 0.4.1
gcsfs            : None
matplotlib       : 3.3.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 2.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.17
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.1
xlrd             : None
xlwt             : None
numba            : 0.51.2

</details>
