fix: convert <NA> values to None instead of stringifying

[eschutho]

SUMMARY

With the pandas pyathena driver, we are seeing some issues with null values for integer columns. The bug is happening at this point where we get a type error

TypeError: Unserializable object <NA> of type <class 'pandas._libs.missing.NAType'> and then we catch the error and try to stringify it, which resulted in pyarrow.lib.ArrowInvalid: Could not convert <NA> with type NAType: did not recognize Python value type when inferring an Arrow data type

For this fix, I am returning None for all pandas values so that the printed value looks like other nullable printed values.

AFTER SCREENSHOTS OR ANIMATED GIF

TESTING INSTRUCTIONS

An athena db with the pyathena+pandas schema should be able to fetch values that are integer types with null values.

ADDITIONAL INFORMATION

• Has associated issue:
• Required feature flags:
• Changes UI
• Includes DB Migration (follow approval process in SIP-59)
  • Migration is atomic, supports rollback & is backwards-compatible
  • Confirm DB migration upgrade and downgrade tested
  • Runtime estimates and downtime expectations provided
• Introduces new feature or API
• Removes existing feature or API

[codecov]

Codecov Report

Merging #22321 (915884d) into master (3bc0865) will decrease coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master   #22321      +/-   ##
==========================================
- Coverage   66.92%   66.90%   -0.02%     
==========================================
  Files        1835     1836       +1     
  Lines       69988    70130     +142     
  Branches     7612     7612              
==========================================
+ Hits        46839    46921      +82     
- Misses      21183    21243      +60     
  Partials     1966     1966              

Flag	Coverage Δ
hive	52.53% <66.66%> (-0.05%)	⬇️
mysql	77.95% <66.66%> (-0.13%)	⬇️
postgres	78.02% <66.66%> (-0.13%)	⬇️
presto	52.42% <66.66%> (-0.05%)	⬇️
python	81.24% <100.00%> (-0.10%)	⬇️
sqlite	76.48% <66.66%> (-0.12%)	⬇️
unit	50.92% <100.00%> (+0.05%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
superset/result_set.py	97.85% <100.00%> (+0.03%)	⬆️
superset/db_engine_specs/databricks.py	64.96% <0.00%> (-17.97%)	⬇️
superset/utils/pandas_postprocessing/utils.py	95.31% <0.00%> (-1.24%)	⬇️
superset/views/database/views.py	31.27% <0.00%> (-0.09%)	⬇️
superset/models/helpers.py	38.19% <0.00%> (-0.09%)	⬇️
superset/charts/schemas.py	99.35% <0.00%> (ø)
superset/connectors/sqla/models.py	89.32% <0.00%> (ø)
superset/utils/pandas_postprocessing/sort.py	100.00% <0.00%> (ø)
superset/db_engine_specs/risingwave.py	100.00% <0.00%> (ø)
superset/charts/data/api.py	89.87% <0.00%> (+0.06%)	⬆️
... and 2 more

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