Extend Tier 3 fingerprinting to midroll ad breaks

Desktop-prototyped first, same discipline as the intro/outro half: three
Darknet Diaries episodes spanning 2026 and one from 2017 were fetched fresh
and cross-correlated. Beyond the show's fixed ~51s intro (present unchanged
across all nine years), the same coarse-to-fine scan found a second, shorter
segment recurring at a *different* timestamp in every episode's body and
independently present in the 2017 episode too -- a real recurring house
promo, not a coincidence of two fetches landing close in time.

That two-episode, different-position, still-present-nine-years-later shape
is exactly what a midroll break looks like and exactly what IntroOutroDetector
was never built to find: it only ever compares fixed head/tail windows.
Midroll detection means scanning a whole episode body, and a show can have
several distinct breaks at once, not one fixed slot per end.

- MidrollScanner: pure, single-resolution "find every segment two recordings
  share, wherever each has it" -- deliberately position-agnostic, built on
  the existing AudioCorrelation primitive rather than a new one.
- Decimate: block-averaging downsample, the coarse half of a coarse-to-fine
  split. A brute-force native-rate scan across a full episode measured
  30-160s per episode *pair* on a desktop CPU in the prototype; MidrollUpdater
  runs MidrollScanner once on ~8x-decimated audio to find candidates cheaply,
  then re-verifies each at full resolution with a single AudioCorrelation
  call -- both for a trustworthy score and to reject anything the decimation
  made look like a match but isn't.
- MidrollDetector: IntroOutroDetector's confirm/discard state machine,
  generalised from one candidate per show to a list. A candidate an episode
  doesn't confirm is discarded outright, same discipline, same reason: the
  Darknet Diaries trap applies here too.
- MidrollUpdater: orchestrates discovery and confirmation per show. Discovery
  compares the show's single rolling "pending" episode against whatever is
  newest; unlike ShowFingerprintEntity's pendingClipPcm, no audio is stored
  for it between runs -- a pending *whole episode* is tens of megabytes of
  PCM, not the few seconds an intro/outro clip costs, so the file is
  re-decoded from disk on demand and the pending slot just moves forward if
  it's since been reclaimed by retention. A real bug the tests caught: the
  same segment surfaces again every time the pending pointer rolls forward,
  which without dedup became a second row for content already tracked.
  Fixed by recognising a fresh discovery hit as already-tracked by *where*
  it landed in the new episode (MidrollDetector.allMatches), not by
  re-correlating extracted clip content, which two different coarse passes
  don't reliably extract the same sub-range of.
- Storage: AppDatabase 7->8, a show_midrolls table (one row per candidate,
  not per show -- several breaks can be tracked at once) and an
  episode_midrolls table (the confirmed ranges detected per episode).
  EpisodeEntity gains midrollCheckedAtMs (per-episode, not a show cursor, so
  an out-of-order backfilled download still gets checked). SubscriptionEntity
  gains midrollPendingEpisodeId.
- Playback: MidrollSkip mirrors AdChapters' shape exactly. PlayheadPolicy
  checks it unconditionally, same as intro/outro -- no per-show toggle, the
  confirmation threshold is the safety mechanism. PlaybackSources grows a
  midrolls() method alongside chapters() rather than threading a new DAO
  through PlaybackAdvancer's constructor, which would have pushed it over
  detekt's LongParameterList threshold for no real reason.
- Trigger: MidrollWorker runs nightly while charging, not per-download like
  FingerprintWorker -- a full-body scan is too expensive to run eagerly, and
  unlike a download it never touches the network, so there is no wifi gate
  either. Peter's call.

MediaCodecClipSource gains decodeFull, sizing the decode buffer from
durationMs with a margin rather than the fixed 4-minute clip window
decodeHead/decodeTail use.

Coverage 91.70% -> 91.76%. Neither half of Tier 3 has run on a device this
session -- see CLAUDE.md's known-gaps list.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Sj3CoWEedM56bebByKgNxA

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