Clear Feathr UDF state and configuration template in work directory

[xiaoyongzhu]

The first issue

In the existing code, we didn't remove generated_feathr_pyspark_metadata when building the features. This is problematic in a few end users, in particular: If UDFs are defined and then are removed, since this file is not cleared, the code will still think there are UDFs, which will either yield wrong results, or exist incorrectly.

This PR makes sure that we remove generated_feathr_pyspark_metadata and a few UDF files every time users build features.

The second issue (#559 )

This is an issue which isn't very obvious. Sometimes after running get_offline_features, then running materialize_features API, the materialize_features API will not be successful, and in many cases there's no values in the online store such as Redis.

This only happens when using databricks.

This is caused by the fact that if the databricks configuration is not a string (i.e. end users use a dict to provide all the required configurations), then there's a line in the code

submission_params = self.config_template

Since self.config_template is a dict, this is actually a reference rather than a copy of self.config_template. In the code later, submission_params will be modified and the value will be carried over across jobs, which will cause different jobs share the same state, and will cause unexpected behaviors.

Other issues

This PR also fixes a few OS compatibility issues (when parsing paths we always assume it's Linux style which isn't true), and fix a few typos.