Classify licenses based on file contents

**What would you like to be added**:
The ability to read entire file contents (or just the top X bytes of the file) and classify the contents as a particular license (e.g. MIT, Apache 2.0, etc). This is a larger addition than https://github.com/anchore/syft/issues/565 (which just covers the SPDX identifiers) but should be thought about together. License content discovered could be persisted optionally in the final SBOM (supported in SPDX).

**Why is this needed**:
Keeping a curated list of licenses for your dependencies is a common use case for SBOMs.

**Additional context**:
Consider using https://github.com/google/licenseclassifier for the heavy lifting.

As a start this could key off of file extensions to filter down to source files (.py, .go, .c, etc) or by filename (e.g. "license", "LICENSE", "license.\.*, etc") to keep the search scope reasonable.

This could be implemented as it's own cataloger that is only responsible for finding licenses in files. This would make the configuration easily accessible, for example:

```yaml
license:
cataloger:
enabled: true
scope: "squashed"

# keep the license content in the final SBOM
capture-content: true

# only search in the following files (by glob)
globs:
- license*
- License*
- *.c
- *.go
- *.py
- *.ts
- *.tsx
...
```

More thought is needed as to how this is organized in the Syft JSON output. That is, does this show up as snippets under packages? Snippets under files? Maybe they get their own section? How does this relate to the `licenses` field under a package? (will it change? relate to another field? or something else?).

Start by reviewing Syft's cataloger configuration and SBOM JSON structure, then examine how package and file license data are currently represented. Define the cataloger's file-selection scope, optional content capture, and output relationship to package licenses; done means the behavior and JSON organization are specified well enough to implement and test.

Written by the indexing model from the issue text.