ENH/API: offsets funcs now accepts datetime64
Even though `CustomBusinessDay.apply` can handle `np.datetime64`, most of other offsets cannot accept `datetime64` and raises `ApplyTypeError`. The fix allows all offsets `apply`, `rollforward` and `rollback` to handle `np.datetime64` properly.

```
import pandas as pd
import numpy as np
t = np.datetime64('2011-01-01 09:00Z')

cday = pd.offsets.CustomBusinessDay()
cday.apply(t)
#2011-01-02 09:00:00

day = pd.offsets.Day()
day.apply(t)
# pandas.tseries.offsets.ApplyTypeError: Unhandled type: datetime64
```

**NOTE:** `CustomBusinessDay` had separate logic for `datetime` and `np.datetime64`. Based on the comparison using current master, `np.datetime64` logic looks slower. Thus I removed it.

```
import timeit
setup = """
import pandas as pd
import numpy as np

cday = pd.offsets.CustomBusinessDay()

np_dt64 = [np.datetime64('2014-05-{0:02} 09:00Z'.format(i)) for i in range(1, 31)]
timestamps = [pd.Timestamp('2014-05-{0:02} 09:00Z'.format(i)) for i in range(1, 31)]
"""

t = timeit.Timer('[cday.apply(d) for d in np_dt64]', setup)
print t.timeit(1000)
#1.6253619194
t = timeit.Timer('[cday.apply(d) for d in timestamps]', setup)
print t.timeit(1000)
#0.959406137466
```

hmm their is a vbench for this
can u add some of the other types to it as well (the other offsets)

I dont recall exactly the speed issue wrt to datetime/dagetime64 but their is a closed issue about it - can u zref the issue and make sure that nothing has changed

thxs
 xref. #6592
 Following is the vbench result. I understand #6592 added 2 separate logic for `datetime` and `np.datetime64`, and both are faster than previous one.

If we compare `datetime` logic and `np.datetime64` logic, `datetime` logic is faster (thus removed `np.datetime64` logic in this PR).

CC @bjonen 

```
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
read_store_table                             |   5.0253 |   4.8374 |   1.0389 |
frame_to_csv_date_formatting                 |  36.4916 |  34.9390 |   1.0444 |
read_store_table_mixed                       |  18.4183 |  17.6213 |   1.0452 |
index_float64_boolean_indexer                |   7.6843 |   7.3157 |   1.0504 |
frame_loc_dups                               |   1.3700 |   1.2767 |   1.0731 |
dtype_infer_uint32                           |   2.6900 |   2.3301 |   1.1545 |
index_str_boolean_series_indexer             |  41.9977 |  35.5417 |   1.1816 |
```
 related to #6318 ?
 @sinhrks can you post the benches that relate to this?
 @hayd Looks different, this is for `datetime64`, not `timedelta`.
@jreback Here it is.

```
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
timeseries_custom_bday_apply_dt64            |   0.0366 |   0.0540 |   0.6779 |
timeseries_custom_bmonthend_incr_n           |   0.2480 |   0.2427 |   1.0219 |
timeseries_custom_bday_incr_n                |   0.0547 |   0.0533 |   1.0253 |
timeseries_custom_bmonthend_incr             |   0.2210 |   0.2154 |   1.0262 |
timeseries_custom_bday_apply                 |   0.0337 |   0.0327 |   1.0316 |
timeseries_custom_bday_incr                  |   0.0363 |   0.0350 |   1.0386 |
-------------------------------------------------------------------------------
```
