BUG: upsampling returns less rows than expected
- [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

I have a pandas Series with a (tz-localized) DateTimeIndex with one value per day, which I want to upsample to hourly values, so that each value is repeated 24 times (or 23 or 25, depending on summer/wintertime changeover).

```python
s = pd.Series([1,2,3,4], index=pd.date_range('2020-03-28 00:00:00', periods=4, freq='D', tz='Europe/Berlin'))
r = s.resample('H').ffill() #misses the last 23 hours of March
```
```
s:
2020-03-28 00:00:00+01:00    1
2020-03-29 00:00:00+01:00    2
2020-03-30 00:00:00+02:00    3
2020-03-31 00:00:00+02:00    4
Freq: D, dtype: int64

r:
2020-03-28 00:00:00+01:00    1
2020-03-28 01:00:00+01:00    1
2020-03-28 02:00:00+01:00    1
                            ..
2020-03-30 22:00:00+02:00    3
2020-03-30 23:00:00+02:00    3
2020-03-31 00:00:00+02:00    4  #<-- !
Freq: H, Length: 72, dtype: int64
```

#### Problem description

Each row in series `s` has the correct number of corresponding rows in series `r` - expect for the final row. The final row in `s` has only 1 corresponding row in `r`, where 24 would be expected.

#### Additional information / observations

a)

I've tried various combinations of having the original and/or the resampled timeseries be open or closed on the right side, but no setting gave me the wanted result.

b)

I assume the reason for the observed behaviour in `r`, above, is the following: I suppose that `freq == D` on a `DatetimeIndex` means, that the values are spaced one calendar day apart, and not, that they are valid for the entire calender day. Using a `PeriodIndex` instead does indeed solve this, see below.

However, using a `PeriodIndex` instead of a `DateTimeIndex` is not an option. It solves the problem with the missing hours, but loses the timezoneawareness that I need:
```python
s2 = pd.Series(s.values, pd.PeriodIndex(s.index, freq='D')) #misses timezoneawareness.
r2 = s2.resample('H').ffill() #does not miss last 23 hours of March, but is still incorrect.
```
```
s2:
2020-03-28    1
2020-03-29    2
2020-03-30    3
2020-03-31    4
Freq: D, dtype: int64

r2:
2020-03-28 00:00    1
2020-03-28 01:00    1
2020-03-28 02:00    1
                   ..
2020-03-31 21:00    4
2020-03-31 22:00    4
2020-03-31 23:00    4
Freq: H, Length: 96, dtype: int64
```

c)

Full disclosure: I've asked this questions before, on [stackoverflow](https://stackoverflow.com/questions/61761237/keep-24h-for-each-day-when-resampling-pandas-series-from-daily-to-hourly), but did not receive a satisfactory answer - just a workaround.

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

<details>


INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en
LOCALE           : de_DE.cp1252

pandas           : 1.0.5
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 49.2.0.post20200714
Cython           : None
pytest           : 5.4.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.3
numexpr          : None
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.17
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
