fix: DML run get timeout if big dataset has more feature columns (Workaround Synapse Spark optimizer issue)

[dylanw-oss]

What changes are proposed in this pull request?

When applying DML on WExp project, the data has >1M records and >5 categorical features, DML run got timeout even with a large cluster.
We figured out that it's due to Synapse version of Spark optimizer won't be able to handle a complex query plan, split DML pipeline and cache each pipeline result, can fix the timeout issue.

How is this patch tested?

with internal project data

[github-actions]

Hey @dylanw-oss 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

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[dylanw-oss]

/azp run

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Azure Pipelines successfully started running 1 pipeline(s).

[codecov-commenter]

Codecov Report

Merging #1903 (6d68c41) into master (0f02626) will increase coverage by 0.05%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master    #1903      +/-   ##
==========================================
+ Coverage   86.77%   86.83%   +0.05%     
==========================================
  Files         301      301              
  Lines       15587    15596       +9     
  Branches      803      815      +12     
==========================================
+ Hits        13526    13543      +17     
+ Misses       2061     2053       -8     

Impacted Files	Coverage Δ
.../azure/synapse/ml/causal/ResidualTransformer.scala	91.89% <ø> (ø)
...rosoft/azure/synapse/ml/train/TrainRegressor.scala	92.59% <ø> (ø)
...ft/azure/synapse/ml/causal/DoubleMLEstimator.scala	89.89% <100.00%> (+0.76%)	⬆️
...osoft/azure/synapse/ml/train/TrainClassifier.scala	84.78% <100.00%> (+0.22%)	⬆️

... and 3 files with indirect coverage changes

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[dylanw-oss]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[dylanw-oss]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[dylanw-oss]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).