Pandas startingMonth ignored on non-unitary Quarter periods

Code Sample

import pandas as pd
import numpy as np

d = pd.Series(data=np.zeros(365), 
                       index=pd.date_range('1950-01-01', '1950-12-31', freq='D'))
d.resample('2QS-MAR').mean()

returns:

1949-12-01    0.0
1950-06-01    0.0
1950-12-01    0.0
Freq: 2QS-MAR, dtype: float64

But as I explicitly asked for 2 Quarters starting in March, I expected:

1949-09-01    0.0
1950-03-01    0.0
1950-09-01    0.0
Freq: 2QS-MAR, dtype: float64
 ```

So, when specifying a multiple, the first time still goes to the closest single quarter before.

Not really a duplicate, but closely related to #22362.

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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-66-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : fr_CA.UTF-8
LOCALE           : fr_CA.UTF-8

pandas           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 9.0.1
setuptools       : 39.0.1
Cython           : None
pytest           : 5.2.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : 0.999999999
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : 0.13.0
xlrd             : None
xlwt             : None
xlsxwriter       : None
</details>


**Comment From: mroeschke**

This looks to work on main now. Could use a test

**Comment From: idor980**

take

**Comment From: idor980**

> This looks to work on main now. Could use a test

I am currently using version 2.0.1 of Pandas and this specific problem isn’t fixed.

**Comment From: joshdelg**

take

**Comment From: quangngd**

Did some digging and i think it is because of the implementation of offset. `rollback` and `rollforward` do not take `n` into account.

```py
offset = QuarterBegin(startingMonth=1, n=2) #2QS
dt = pd.Timestamp("1950-01-02")
offset.rollback(dt), offset.rollforward(dt)

and

offset = QuarterBegin(startingMonth=1, n=1) #QS
dt = pd.Timestamp("1950-01-02")
offset.rollback(dt), offset.rollforward(dt)

both return (Timestamp('1950-01-01 00:00:00'), Timestamp('1950-04-01 00:00:00')).