Dynamically configure precision

[halfabrane]

There are use cases where within the same Spark run (ie with the same spark context) we want to join multiple different spatial datasets at different resolutions.
This PR allows you to pass in the resolution as a hint as follows:

import org.apache.spark.sql.magellan.dsl.expressions._
point.join(polygons index 15).where($"point" within $"polygon")

allows you to pass a hint to the optimizer that the index resolution should be set to 15.

also, modifies injectRules to pass in only the spark session and not the parameters.
ie. use injectRules(spark) instead of injectRules(spark, Map(...))

Future enhancements:

• Ability to pass in a min/ max precision to greedily cover the geometries?
• Ability to index geometries at the right precision on the fly?

[codecov-io]

Codecov Report

Merging #145 into master will increase coverage by 0.09%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #145      +/-   ##
==========================================
+ Coverage   85.47%   85.56%   +0.09%     
==========================================
  Files          47       48       +1     
  Lines        1363     1372       +9     
  Branches       96       96              
==========================================
+ Hits         1165     1174       +9     
  Misses        198      198

Impacted Files	Coverage Δ
...main/scala/magellan/catalyst/SpatialJoinHint.scala	100% <100%> (ø)
src/main/scala/magellan/Utils.scala	100% <100%> (ø)	⬆️
src/main/scala/magellan/dsl/package.scala	84.61% <100%> (+4.61%)	⬆️
src/main/scala/magellan/catalyst/SpatialJoin.scala	100% <100%> (ø)	⬆️

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[harsha2010]

@Perados can you give this a try when you get a chance? this might cover your use case nicely