Do explicit check for missing values for tool / continuous column types

[surchs]

we need to add an explicit check for missing values for the tool / continuous column types - similar to what we have done for discrete values.

According to our current data dictionary docs + reference example .json, any phenotypic column that's not an identifier column can have annotated missing values (I believe the CLI allows this as well). Based on the bulk_annotation_levels gsheet, I see that there's at least one assessment tool (e.g., ds003823 "ses-pre_task-SART" column) with a missing value annotated. Not sure how/if these are currently handled.

Steps:

• [ ] Change the way we generate the bulk_annotation_levels to give us a (better) chance of seeing missing values for columns about "age" or other numeric types
• [ ] update the process_annotation_to_dict.py so it handles missing value annotations for assessment tool columns (those that have "isPartOf") and age columns
• [ ] regenerate data dictionaries and push the processed files to the graph

_Originally posted by @alyssadai in https://github.com/neurobagel/bulk_annotations/pull/24#discussion_r1255037484_

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