Add base methods for iterator serialization

[CodyCBakerPhD]

Motivation

Zarr supports efficient parallelization, and a sister PR (hdmf-dev/hdmf-zarr#111) adds this capability but requires the argument number_of_jobs to be propagated from a top level of the HDMFIO.write due to the super call at this line

It also has a subtle condition in that our iterators (which in this role primarily define how to access data from a source) must be pickleable, so I've defined methods related to that for the GenericDataChunkIterator (which for several reasons should be the only type of iterator allowed for usage in parallelization)

An example in practice can be found downstream: catalystneuro/neuroconv#536

Checklist

• Did you update CHANGELOG.md with your changes?
• Have you checked our Contributing document?
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[codecov]

Codecov Report

Patch coverage: 100.00% and project coverage change: +0.01% 🎉

Comparison is base (ca7722f) 88.33% compared to head (434a21f) 88.34%.

Additional details and impacted files

@@            Coverage Diff             @@
##              dev     #924      +/-   ##
==========================================
+ Coverage   88.33%   88.34%   +0.01%     
==========================================
  Files          45       45              
  Lines        9284     9292       +8     
  Branches     2651     2653       +2     
==========================================
+ Hits         8201     8209       +8     
  Misses        765      765              
  Partials      318      318              

Files Changed	Coverage Δ
src/hdmf/backends/io.py	96.47% <100.00%> (ø)
src/hdmf/data_utils.py	90.42% <100.00%> (+0.17%)	⬆️

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[CodyCBakerPhD]

@rly @oruebel Ready for review