Fix the inverted EDUCQUAL to highest_education mapping

[juaristi22]

Fixes #459.

The EDUCQUAL_MAP read the FRS highest-qualification codeframe upside down: code 1 was mapped to NOT_COMPLETED_PRIMARY and codes 17–21 to TERTIARY, but the frame starts at 1 = Doctorate and descends. highest_education feeds live model logic (maintenance loan system assignment, DSA course eligibility, student loan plan assignment).

This cherry-picks the existing correction from feat/impute-below-threshold-borrowers (8f95629, authorship preserved) onto main, now verified two independent ways:

• The official data dictionary: FRS 2023-24, UK Data Service SN 9367, DOI 10.5255/UKDA-SN-9367-2, adult table — every label in the corrected map matches verbatim (1 = Doctorate or MPhil, 17 = A-Level or equivalent, 18/19 = Welsh/Scottish Baccalaureate, 36–49 the GCSE band, 83–85 entry level). Code 87, carried by ~13% of adults, has no value label in the dictionary and falls to the UPPER_SECONDARY default.
• Disclosure-safe aggregates from the 2023-24 tab (28,590 adults): code 1 is 1.8% of adults with the highest mean weekly earnings (~£744); codes 18/19 have fewer than 10 respondents each; the GCSE band carries the population mass. Under the inverted map the weighted TERTIARY share came out near 10%; the corrected frame gives ~28%.

Under the corrected map the weighted highest_education composition of the base FRS moves from {TERTIARY 9.9%, POST_SECONDARY 21.3%} to {TERTIARY 28.1%, POST_SECONDARY 8.4%} — expect downstream shifts in student-support and education-conditioned outputs on the next data build.

The microcosm migration adopted the same corrected frame as a signed difference in PolicyEngine/microcosm#703, so landing this keeps the two pipelines aligned.

Test suite: 484 passed, 107 skipped (licensed-data tests skip without staged inputs), no failures.

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