Strange behaviour of pd.offsets.CustomBusinessHour when using holidays
#### Code Sample, a copy-pastable example if possible

```python
import datetime
import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar

bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar())
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),start='07:00')
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21'])
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21','2014-01-22'])
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21','2014-02-06'])

dt = datetime.datetime(2014, 1, 17, 13)

for i in range(1,30):
    y = dt + bhour_us * i
    print(y)
```
>>>
2014-01-17 14:00:00
2014-01-17 15:00:00
2014-01-17 16:00:00
2014-01-21 09:00:00
2014-01-21 10:00:00
2014-01-21 11:00:00
2014-01-21 12:00:00
**_2014-01-20 13:00:00
2014-01-31 14:00:00
2014-01-31 15:00:00
2014-01-31 16:00:00
2014-02-03 09:00:00
2014-02-03 10:00:00
2014-02-03 11:00:00
2014-02-03 12:00:00_**
2014-01-21 13:00:00
2014-01-21 14:00:00
2014-01-21 15:00:00
2014-01-21 16:00:00
2014-01-22 09:00:00
2014-01-22 10:00:00
2014-01-22 11:00:00
2014-01-22 12:00:00
2014-01-22 13:00:00
2014-01-22 14:00:00
2014-01-22 15:00:00
2014-01-22 16:00:00
2014-01-23 09:00:00
2014-01-23 10:00:00

#### Problem description
CustomBusinessHour behaves strangely when I try to use a custom calendar (same behaviour when using a list of holidays directly).

In the example above, I modified the example from the official doc.
https://pandas.pydata.org/pandas-docs/version/0.18.1/timeseries.html#timeseries-custombusinesshour

Something strange happens (here at i in range(8, 16)).

Then, the rest is fine, but shifted away.

Playing with the calendar and length of the business day (start and end) affects this.
This seems related in some sense to a mod function somewhere... But I can't get my finger on it.
#### Expected Output
2014-01-17 14:00:00
2014-01-17 15:00:00
2014-01-17 16:00:00
2014-01-21 09:00:00
2014-01-21 10:00:00
2014-01-21 11:00:00
2014-01-21 12:00:00
2014-01-21 13:00:00
2014-01-21 14:00:00
2014-01-21 15:00:00
2014-01-21 16:00:00
2014-01-22 09:00:00
2014-01-22 10:00:00
2014-01-22 11:00:00
2014-01-22 12:00:00
2014-01-22 13:00:00
2014-01-22 14:00:00
2014-01-22 15:00:00
2014-01-22 16:00:00
2014-01-23 09:00:00
2014-01-23 10:00:00
2014-01-23 11:00:00
2014-01-23 12:00:00
2014-01-23 13:00:00
2014-01-23 14:00:00
2014-01-23 15:00:00
2014-01-23 16:00:00
2014-01-24 09:00:00
2014-01-24 10:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.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           : None.None

pandas           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 42.0.1.post20191125
Cython           : 0.29.14
pytest           : 5.3.0
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : 1.2.6
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : 1.3.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.1
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.11
tables           : 3.6.1
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.6
</details>