Incorrect timedelta computation of pd.Series datetime64[ns] if timedelta is too large
#### Problem description

Below I present two methods to calculate the time difference between two `pd.Timestamp`'s. The first way is the native python `datetime` subtraction, whereas the second way wraps things in a `pd.Series`. They should be equivalent. In example 1 the output is correct; but in example 2 the resulting timedelta is negative. This seems to be the case when the time difference  is greater than ~300 years. 

#### Code Sample
Example 1:
```python
t0 = pd.Timestamp('1723-08-23 00:00:00')
t1 = pd.Timestamp('2013-08-23 00:00:00')
print( 'Expected', (t1 - t0).total_seconds() )
print( 'Got\t', (pd.Series([t1]) - pd.Series([t0]))[0].total_seconds() )
```

Output 1:
```
Expected 9151574400.0
Got	 9151574400.0
```

Whereas Example 2:
```python
t0 = pd.Timestamp('1713-08-23 00:00:00')
t1 = pd.Timestamp('2013-08-23 00:00:00')
print( 'Expected', (t1 - t0).total_seconds() )
print( 'Got\t', (pd.Series([t1]) - pd.Series([t0]))[0].total_seconds() )
```
Output 2:
```
Expected 9467107200.0
Got	 -8979636873.709553
```


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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-130-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 18.0
setuptools: 40.2.0
Cython: None
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
