New analyzer: HashScanner (NIST NSRL known-file lo

Feature description
Add a new analyzer, HashScanner_NSRL, for the hash data type (MD5 / SHA-1 / SHA-256). It looks up an observable against the NIST National Software Reference Library (NSRL) via the HashScanner API and reports whether the file is known (cataloged in NSRL). This lets analysts filter known files out of a case and focus on the unknown. A match means the file is known — not a safe/clean/malicious verdict.

Describe the solution you'd like
A standard Cortex analyzer (Python + cortexutils) with:

• Flavor HashScanner_NSRL (data type hash), API key configured per organization
• A single taxonomy: HashScanner:NSRL = Known / Unknown
• Short and long report templates

Free API tier available (registration required). I have the analyzer ready (flavor JSON, program, requirements, report templates, in catalog format) and will open a PR against develop referencing this issue.

Additional context
HashScanner provides the NSRL online so teams don't have to download and maintain the ~700 GB Reference Data Set locally. Reference repo: https://github.com/hashscanner/hashscanner-cortex

Start with the prepared flavor JSON, program, requirements, report templates, and catalog-format files in the referenced hashscanner-cortex repository. Verify that the analyzer uses the HashScanner API for hash observables and produces the specified Known/Unknown taxonomy and short and long reports.