DatetimeIndex: 'freq' inconsistencies
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
from pandas.tseries.offsets import *

rng = pd.date_range('2012-01-05', '2012-01-10', freq=BDay())
index = pd.DatetimeIndex(rng)
print(index)

index2 = index + pd.Timedelta('1D')
print(index2)
```
#### Problem description

The line `index + pd.Timedelta('1D')` adds 1 day to a DatetimeIndex that has `freq='B'` but the returned index contains non-business days:

DatetimeIndex(['2012-01-06', '2012-01-07', '2012-01-10', '2012-01-11'], dtype='datetime64[ns]', freq='B')

Please note that the `freq` property is  `B`. 

This behavior is very inconsistent:
-  Why isn't `freq` considered when performing computation (+/- Timedelta) on the DatetimeIndex? 
-  Why `freq` doesn't reflect the actual data contained in the DatetimeIndex? ( it says 'B' even though it contains non-business days)

#### Expected Output
DatetimeIndex(['2012-01-05', '2012-01-06', '2012-01-09', '2012-01-10'], dtype='datetime64[ns]', freq='B')
DatetimeIndex(['2012-01-06', '2012-01-09', '2012-01-10', '2012-01-12'], dtype='datetime64[ns]', freq='B')

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-26-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: 3.0.6
pip: 9.0.1
setuptools: 38.2.4
Cython: 0.23.4
numpy: 1.14.0
scipy: 0.18.1
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.5
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 1.0b10
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.5.0

</details>
