Allow lazy materialization of iterated CSV.Chunks

While discussing out-of-core data processing with @bkamins, we realized it's currently a bit awkward to work with CSV.Chunks. For context, CSV.Chunks is currently structured to:

• parse the header and initial data positions for each chunk in CSV.Chunks constructor
• When CSV.Chunks is iterated, the CSV.Context + byte position is passed to CSV.File for parsing and CSV.File is returned

That means it's really difficult, if not impossible, if you wanted to spawn the chunk parsing to a separate thread/remote process, since it's baked in to the call to iterate. It would be more helpful if we structured it more similarly to Tables.partitioner(f, iter), where f(x) generates a LazyTable and the table isn't materialized until Tables.columns or Tables.rows is called. So the API for CSV.Chunks would become:

• chunks = CSV.Chunks(...)
• x, st = iterate(chunks)
• file = CSV.materialize(x) where materialize takes x, which would be some kind of CSV.Chunk object that noted the CSV.Context and starting byte position, and materialize would call the actual CSV.File parsing

Start by reading the CSV.Chunks constructor and iterate implementation, then compare the proposed shape with Tables.partitioner(f, iter). Define how iteration yields a lazy CSV.Chunk carrying the CSV.Context and byte position, and how CSV.materialize(x) produces the parsed CSV.File; done means chunk parsing is deferred until materialization.