Pandas Timedelta gives different result than numpy timedelta64
Is this the desired behavior of pandas vs numpy timedelta?

```python

In [4]: df = pd.DataFrame({'date': pd.date_range('2021-05-16',periods=5)})

In [5]: df
Out[5]: 
        date
0 2021-05-16
1 2021-05-17
2 2021-05-18
3 2021-05-19
4 2021-05-20

In [6]: df['date'] - pd.Timedelta('12 m')
Out[6]: 
0   2021-05-15 23:48:00
1   2021-05-16 23:48:00
2   2021-05-17 23:48:00
3   2021-05-18 23:48:00
4   2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]

In [7]: df['date'] - pd.Timedelta('12 M') # Pandas gives same result for m and M
Out[7]: 
0   2021-05-15 23:48:00
1   2021-05-16 23:48:00
2   2021-05-17 23:48:00
3   2021-05-18 23:48:00
4   2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]

In [8]: df['date'] - np.timedelta64(12,'m')
Out[8]: 
0   2021-05-15 23:48:00
1   2021-05-16 23:48:00
2   2021-05-17 23:48:00
3   2021-05-18 23:48:00
4   2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]

In [9]: df['date'] - np.timedelta64(12,'M') # numpy gives DIFFERENT result for m and M
Out[9]: 
0   2020-05-15 18:10:48
1   2020-05-16 18:10:48
2   2020-05-17 18:10:48
3   2020-05-18 18:10:48
4   2020-05-19 18:10:48
Name: date, dtype: datetime64[ns]
```