Adding offset to DatetimeIndex is broken
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
offset = pd.DateOffset(months=3, days=10)
dates = pd.date_range(start='01 Jan 2014', end='01 Jan 2017', freq='AS')
dates + offset
```

#### Problem description

ValueError is raised:

```python

Traceback (most recent call last):
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\arrays\datetimelike.py", line 884, in _validate_frequency
    raise ValueError
ValueError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\indexes\datetimelike.py", line 489, in __add__
    result = self._data.__add__(maybe_unwrap_index(other))
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\arrays\datetimelike.py", line 1190, in __add__
    result = self._add_offset(other)
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\arrays\datetimes.py", line 737, in _add_offset
    result = offset.apply_index(values)
  File "pandas/_libs/tslibs/offsets.pyx", line 116, in pandas._libs.tslibs.offsets.apply_index_wraps.wrapper
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\tseries\offsets.py", line 278, in apply_index
    i = type(i)(shifted, freq=i.freq, dtype=i.dtype)
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\arrays\datetimes.py", line 351, in __init__
    type(self)._validate_frequency(self, freq)
  File "C:\mma\local\Anaconda3\envs\pd24\lib\site-packages\pandas\core\arrays\datetimelike.py", line 897, in _validate_frequency
    .format(infer=inferred, passed=freq.freqstr))
ValueError: Inferred frequency AS-APR from passed values does not conform to passed frequency AS-JAN
```

#### Expected Output

This is what had been working through version 0.22.0 and was broken somewhere between 0.22 and 0.24:

```python
>>> dates + offset
DatetimeIndex(['2014-04-11', '2015-04-11', '2016-04-11', '2017-04-11'], dtype='datetime64[ns]', freq=None)
```
#### Workaround

```python

>>> pd.DatetimeIndex([date + offset for date in dates])
DatetimeIndex(['2014-04-11', '2015-04-11', '2016-04-11', '2017-04-11'], dtype='datetime64[ns]', freq=None)
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
