Cache the block order and skip unchanged nested regions in ConstantPropagation

[ThrudPrimrose]

Constant collection is a fixpoint over each control flow region, and it recursed into nested regions
from inside the fixpoint loop. So every sweep of every region re-ran blockorder_topological_sort,
which computes immediate dominators and branch merges over the region, and a region nested d levels
deep was re-analysed on every sweep at every enclosing level.

Neither is needed. The CFG does not change while constants are being collected, so the block order is
the same on every sweep and can be cached; and a nested region is a function of its incoming
constants, so an unchanged input reaches the same fixpoint and does not need re-collecting. The
transfer and meet rules are untouched — this only changes the schedule.

Measured on the CloudSC kernel, parsed once with to_sdfg(simplify=False) and deepcopied fresh for
every timed run, arms interleaved rep by rep. Median (min–max), 5 reps:

unsimplified, 6342 blocks / 5890 states
  apply_pass    old  4.93s (4.77-5.11)    new  1.66s (1.61-1.72)    2.97x

after ScalarToSymbolPromotion + ControlFlowRaising, 6612 blocks
  apply_pass    old 24.16s (23.21-24.74)  new  8.72s (8.16-8.86)    2.77x

SimplifyPass, 4 reps
                old 84.8s (82.7-92.3)     new 76.1s (74.8-78.9)     1.11x

The box was not idle, so treat the absolute times as loaded; within-arm spread is under 5% and every
rep interleaves the arms, so the ratios hold.

Old and new produce identical output: the same propagated-symbol set, and equal SDFGs across symbols,
data descriptors, compile-time constants, blocks, interstate conditions and assignments, dataflow
nodes, tasklet code, memlets, loop headers and conditional branches.

Split out of #2470 so the scheduling change can be reviewed and merged on its own; the CloudSC corpus,
its integration tests and the timing budget stay there and rebase onto this.

[acalotoiu]

LGTM! Awesome!