Rounding errors with Timestamps and `.last()`
#### Code Sample, a copy-pastable example if possible

```python
In [1]: import pandas as pd 
In [3]: ts = pd.Timestamp('2016-10-14 21:00:44.557')                                   
In [4]: pd.DataFrame({'a': range(2), 'b': [ts, pd.NaT]}).groupby('a').b.last()         
Out[4]:                                                                                
a                                                                                      
0   2016-10-14 21:00:44.556999936                                                      
1                             NaT                                                      
Name: b, dtype: datetime64[ns]   
```
#### Problem description
The value of the Timestamp in the output is not equal to the value in the input. 

#### Expected Output
```python
Out[4]:                                                                                
a                                                                                      
0   2016-10-14 21:00:44.557                                                            
1                       NaT                                                            
Name: b, dtype: datetime64[ns]
```
The issue is a bit subtle. Here are three related examples, all of which **do** work as expected:

I haven't been able to repro with `.nth(-1)` in place of `last()`:

```python
In [1]: import pandas as pd 
In [3]: ts = pd.Timestamp('2016-10-14 21:00:44.557') 
In [5]: pd.DataFrame({'a': range(2), 'b': [ts, pd.NaT]}).groupby('a').b.nth(-1)        
Out[5]:                                                                                
a                                                                                      
0   2016-10-14 21:00:44.557                                                            
1                       NaT                                                            
Name: b, dtype: datetime64[ns]
```

I haven't been able to repro if `NaT` is not present:
```
In [6]: pd.DataFrame({'a': range(2), 'b': [ts, ts]}).groupby('a').b.last()
Out[6]: 
a
0   2016-10-14 21:00:44.557
1   2016-10-14 21:00:44.557
Name: b, dtype: datetime64[ns]
```

I haven't been able to repro for times which are integer seconds:
```
In [7]: ts = pd.Timestamp('2016-10-14 21:00:44')
In [8]: pd.DataFrame({'a': range(2), 'b': [ts, pd.NaT]}).groupby('a').b.last()
Out[8]: 
a
0   2016-10-14 21:00:44
1                   NaT
Name: b, dtype: datetime64[ns]
```

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

<details>

```
INSTALLED VERSIONS                                                                     
------------------                                                                     
commit: None                                                                           
python: 3.6.3.final.0                                                                  
python-bits: 64                                                                        
OS: Linux                                                                              
OS-release: 4.13.0-32-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: 3.2.1                                                                          
pip: 9.0.1                                                                             
setuptools: 38.2.4                                                                     
Cython: 0.26.1                                                                         
numpy: 1.14.0                                                                          
scipy: 0.19.1                                                                          
pyarrow: None                                                                          
xarray: None                                                                           
IPython: 6.1.0                                                                         
sphinx: 1.6.3                                                                          
patsy: 0.4.1                                                                           
dateutil: 2.6.1                                                                        
pytz: 2017.3                                                                           
blosc: None                                                                            
bottleneck: 1.2.1                                                                      
tables: 3.4.2                                                                          
numexpr: 2.6.2                                                                         
feather: None                                                                          
matplotlib: 2.1.0                                                                      
openpyxl: 2.4.8                                                                        
xlrd: 1.1.0                                                                            
xlwt: 1.3.0                                                                            
xlsxwriter: 1.0.2                                                                      
lxml: 4.1.0                                                                            
bs4: 4.6.0                                                                             
html5lib: 0.9999999                                                                    
sqlalchemy: 1.1.13                                                                     
pymysql: None                                                                          
psycopg2: None                                                                         
jinja2: 2.9.6                                                                          
s3fs: None                                                                             
fastparquet: None                                                                      
pandas_gbq: None                                                                       
pandas_datareader: None
```
</details>
