ENH: read_csv parse_dates should use datetime64[us] instead of datetime64[ns] if date out of bound is detected.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np

df = pd.read_csv("./Artikelstatus_Augsburg.csv", sep=';', parse_dates=['VON_DTM','BIS_DTM'], infer_datetime_format=True)
print(df.info())
print(df.head())
```

```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14472 entries, 0 to 14471
Data columns (total 4 columns):
Artikel     14472 non-null int64
ATTRIBUT    14472 non-null int64
VON_DTM     14472 non-null datetime64[ns]
BIS_DTM     14472 non-null object
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 452.3+ KB
None
   Artikel  ATTRIBUT             VON_DTM              BIS_DTM
0       17         0 2018-10-01 00:00:00  2018-11-21 16:25:18
1       17         3 2018-11-21 16:25:19  2999-12-31 23:59:59
2       35         0 2018-10-01 00:00:00  2018-11-30 11:38:21
3       35         2 2018-11-30 11:38:22  2018-12-04 17:09:05
4       35         0 2018-12-04 17:09:06  2018-12-05 11:09:24
```


#### Problem description
`read_csv` with parameteter `parse_dates` uses `datetime64[ns]` as default. That datatype, however, only covers dates between the years 1678 an 2262. Many systems use hardcoded special dates for `-Inf` or `Inf`, including `0001-01-01 00:00:00`, `2999-12-31 23:59:59` or `9999-12-31 23:59:59`.

The current behavior of `read_csv` is that an `object` column instead of a `datetime64[us]` is returned. Using the `date_parser` argument leads to a huge performance drop and even solutions like `na_values = ['2999-12-31 23:59:59']` do not work.

Instead of an `object` column, a `datetime64[us]` column should be returned, when an out-of-bounds date is found. Possible approaches to solve the issue include autodectection by `csv.Sniffer`, a separate parameter `datetime_unit` or backtracking.

#### Expected Output

```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14472 entries, 0 to 14471
Data columns (total 4 columns):
Artikel     14472 non-null int64
ATTRIBUT    14472 non-null int64
VON_DTM     14472 non-null datetime64[ns]
BIS_DTM     14472 non-null datetime64[us]
dtypes: datetime64[ns](1), datetime64[us](1), int64(2)
memory usage: 452.3+ KB
None
   Artikel  ATTRIBUT             VON_DTM              BIS_DTM
0       17         0 2018-10-01 00:00:00  2018-11-21 16:25:18
1       17         3 2018-11-21 16:25:19  2999-12-31 23:59:59
2       35         0 2018-10-01 00:00:00  2018-11-30 11:38:21
3       35         2 2018-11-30 11:38:22  2018-12-04 17:09:05
4       35         0 2018-12-04 17:09:06  2018-12-05 11:09:24
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1052-gcp
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.1
pytest: None
pip: 19.2.2
setuptools: 41.0.1
Cython: 0.29.14
numpy: 1.16.4
scipy: 1.3.1
pyarrow: 0.15.1
xarray: None
IPython: 7.8.0
sphinx: 2.2.0
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.10
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
