BUG: nonexistent Timestamp pre-summer/winter DST w/dateutil timezone
- [X] closes #31043
- [X] tests added 1 / passed 1
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry

This implements rounding down to microseconds into Timedelta.total_seconds(). Lack of this rounding led to `dateutil.tz.tzinfo.utcoffset` and `dst` breaking less than 128 nanoseconds before winter/summer DST switch. This happened due to an unintended cast to float in `Timedelta.total_seconds()` compounded with `Timedelta.value` being np.int64 type which doesn't support long arithmetic. The loss of precision led to rounding up, which meant that `total_seconds` since epoch time implied DST time while the `Timedelta.value` hasn't yet reached DST.

Details and code examples below.

<details>

This was quite a journey, but I found out what's going on. Let's say we have a `dateutil.tz.tzinfo` object named `du_tz` and want to find out the DST-aware UTC offset.

1. We call `du_tz.utcoffset(dt)` which calls `du_tz._find_ttinfo(dt)` which calls `du_tz._resolve_ambiguous_time(dt)` to find the index of the last transition time that it uses to return the correct offset.
2. `du_tz._resolve_ambiguous_time(dt)` calls `du_tz._find_last_transition(dt)` which calls the `_datetime_to_timestamp(dt)` dateutil function.
3. This is what this function does:

```
def _datetime_to_timestamp(dt):
    """
    Convert a :class:`datetime.datetime` object to an epoch timestamp in
    seconds since January 1, 1970, ignoring the time zone.
    """
    return (dt.replace(tzinfo=None) - EPOCH).total_seconds()
```

The problem is dateutil's reliance on `Timedelta.total_seconds` which casts to float:

```
    def total_seconds(self):
        """
        Total duration of timedelta in seconds (to ns precision).
        """
        return self.value / 1e9
```

Demonstration:

```
IN:
import datetime
import pandas as pd

epoch =  1552211999999999872
ts = pd.Timestamp(epoch, tz='dateutil/US/Pacific')

EPOCH = datetime.datetime.utcfromtimestamp(0)

delta = ts.replace(tzinfo=None) - EPOCH
print(delta.value)
OUT:
1552183199999999872
IN:
print(delta.total_seconds())
OUT:
1552183200.0
IN:
print(ts.tz.dst(ts))
OUT:
datetime.timedelta(seconds=3600)
```
The same thing happens with a `pytz` timezone, only pytz relies on something else to check for DST transitions:
```
IN:
import datetime
import pandas as pd

epoch =  1552211999999999872
ts = pd.Timestamp(epoch, tz='US/Pacific')

EPOCH = datetime.datetime.utcfromtimestamp(0)

delta = ts.replace(tzinfo=None) - EPOCH
print(delta.value)
OUT:
1552183199999999872
IN:
print(delta.total_seconds())
OUT:
1552183200.0
IN:
print(ts.tz.dst(ts))
OUT:
datetime.timedelta(0)
```
So `timedelta` value is okay, but `timedelta.total_seconds` hits the precision limit of floats, and this leads to rounding and an incorrect DST offset.

</details>

@jreback 
Okay, I found why this breaks on minimum versions and macOS. Both specify `python-dateutil == 2.6.1`.
Before version 2.7.0, `dateutil` didn't fully rely on `timedelta.total_seconds()`. Instead it implemented this function:
```
def _total_seconds(td):
    # Python 2.6 doesn't have a total_seconds() method on timedelta objects
    return ((td.seconds + td.days * 86400) * 1000000 +
            td.microseconds) // 1000000


_total_seconds = getattr(timedelta, 'total_seconds', _total_seconds)
```
They had this for Python 2.6 datetime support and dropped it in [this PR](https://github.com/dateutil/dateutil/pull/362).
This implementation grabs `datetime.timedelta.total_seconds()` with `getattr`, and we can't influence this from the pandas side.

I set up a python 3.6 environment and tested this hypothesis. The tests fail with dateutil 2.6.1 and pass with dateutil 2.7.0.

Basically, I want to know how I should proceed.
I don't see a way to work around this without overriding dateutil's `tzinfo` methods, and this seems like a horrible idea.
Or we could change our minimum supported version of dateutil to 2.7.0 if that's acceptable. We don't support Python 2 in pandas, so there is really no need to support a version of dateutil this low.
I wonder why we specify version 2.6.1 for macOS though. so just change the test have different cases for >=2,7 and <2,7
we already moved to
2.6.1 as the min (for 1.0.0);
 Because `dateutil.__version__` breaks the `mypy` check, I'm using `pandas.compat._optional._get_version`. Seems like it was written precisely for something like this, and it's certainly a better idea than importing `pkg_resources.get_distribution`.
Once the numpy dev PR is merged, I'll merge master into my branch, and we should be green. @jreback 
All green, please review. thanks @AlexKirko very nice