Towards consistent null handling

This PR includes a number of fixes to handle nulls more consistently on the query side.  These fixes were done to support a user who is leveraging the schema-less capabilities of Druid.  It's sufficient for all needs we've currently found, but I am not certain that it is comprehensive just yet.

Fixes #665 in the following manner

- return null values for all matching rows when a columns is missing (e.g. for groupBy, topN)
- treat empty string and null the same, but always return null
- allow specifying null in filters

- for multi-value dimensions, if the dimension has multiple values:   
     1. dim extraction returns null -> null
     2. missing column -> null
     3. [] -> null
     4. [""] -> [null]
     5. ["", "a"] -> [null, "a"]

- for single value dimensions
    1. missing column -> null
    2. dim extraction returns null -> null
    3. [""] -> null
    4. [] -> null



Also, on the PR is the addition of a context parameter on timeseries queries that allows it to ignore empty buckets.  I realize that this should've been separated into two PRs, but there is a bit of context behind these commits that makes that difficult.  It's just a few changes in `TimeseriesQuery`, `TimeseriesQueryEngine` and `TimeseriesQueryRunnerTest`
@cheddar : not that filters to include xyz, but rather if you have a dimension, let's say "cake", which never actually have a value for (all null) and you say "give me all events where cake is *not* cheesecake", then I would expect it to return all events.

This contradicts traditional sql, where the not of a null is a null.