BUG: resample seems to convert hours to 00:00 
- [x] I have checked that this issue has not already been reported. (As far as I can see by using the search)

- [x] I have confirmed this bug exists on the latest version of pandas.

- [x] (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
# Your code here
import datetime as dt
import pandas as pd

start_time = dt.datetime(year=2018, month=6, day=7, hour=18)
end_time = dt.datetime(year=2018, month=6, day=27, hour=18)
time_index_df = pd.date_range(
    start_time,
    end_time,
    freq="12H",
    name="datetime",
).to_frame(index=False)

time_index_df["test"] = 1
time_index_df["name"] = "Name"

time_index_df = time_index_df.resample("2d", convention="end", on="datetime").mean().reset_index()

print(time_index_df["datetime"].values)

time_index_df.to_csv(f"test_resample_{pd.__version__}")

```

#### Problem description
On pandas 0.23 the behaviour of resample will keep the correct datetime (18:00 in this case) after resample.
But starting from 0.24, after resample, the datetime is now converted to (00:00).
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]

#### Expected Output
On. 0.23
['2018-06-07T18:00:00.000000000' '2018-06-09T18:00:00.000000000'
 '2018-06-11T18:00:00.000000000' '2018-06-13T18:00:00.000000000'
 '2018-06-15T18:00:00.000000000' '2018-06-17T18:00:00.000000000'
 '2018-06-19T18:00:00.000000000' '2018-06-21T18:00:00.000000000'
 '2018-06-23T18:00:00.000000000' '2018-06-25T18:00:00.000000000'
 '2018-06-27T18:00:00.000000000']

But Starting from 0.24 it is giving me
['2018-06-07T00:00:00.000000000' '2018-06-09T00:00:00.000000000'
 '2018-06-11T00:00:00.000000000' '2018-06-13T00:00:00.000000000'
 '2018-06-15T00:00:00.000000000' '2018-06-17T00:00:00.000000000'
 '2018-06-19T00:00:00.000000000' '2018-06-21T00:00:00.000000000'
 '2018-06-23T00:00:00.000000000' '2018-06-25T00:00:00.000000000'
 '2018-06-27T00:00:00.000000000']


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


<details>


0.23
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 5.3.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8

pandas: 0.23.0
pytest: None
pip: 20.0.2
setuptools: 46.0.0
Cython: None
numpy: 1.18.5
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.8
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: 2.8.5 (dt dec pq3 ext lo64)
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None


0.24 
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 5.3.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8

pandas: 0.24.2
pytest: None
pip: 20.0.2
setuptools: 46.0.0
Cython: None
numpy: 1.18.5
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.8
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.19
pymysql: None
psycopg2: 2.8.5 (dt dec pq3 ext lo64)
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None


</details>
