[mlir][dataflow] Consolidate AbstractSparseLattice::markPessimisticFixpoint()...

[mlir][dataflow] Consolidate AbstractSparseLattice::markPessimisticFixpoint() and AbstractDenseLattice::reset() into Abstract{Sparse,Dense}DataFlowAnalysis::setToEntryState().

### Rationale

For a program point where we cannot reason about incoming dataflow (e.g. an argument of an entry block), the framework needs to initialize the state.

Currently, `AbstractSparseDataFlowAnalysis` initializes such state to the "pessimistic fixpoint", and `AbstractDenseDataFlowAnalysis` calls the state's `reset()` function.

However, entry states aren't necessarily the pessimistic fixpoint. Example: in reaching definition, the pessimistic fixpoint is `{all definitions}`, but the entry state is `{}`.

This awkwardness might be why the dense analysis API currently uses `reset()` instead of `markPessimisticFixpoint()`.

This patch consolidates entry point initialization into a single function `setToEntryState()`.

### API Location

Note that `setToEntryState()` is defined in the analysis rather than the lattice, so that we allow different analyses to use the same lattice but different entry states.

### Removal of the concept of optimistic/known value

The concept of optimistic/known value is too specific to SCCP.

Furthermore, the known value is not really used: In the current SCCP implementation, the known value (pessimistic fixpoint) is always `Attribute{}` (non-constant). This means there's no point storing a `knownValue` in each state.

If we do need to re-introduce optimistic/known value, we should put it in the SCCP analysis, not the sparse analysis API.

### Terminology

Please let me know if "entry state" is a good terminology.

I chose "entry" from Wikipedia (https://en.wikipedia.org/wiki/Data-flow_analysis#Basic_principles).

Another term I can think of is "boundary" (https://suif.stanford.edu/~courses/cs243/lectures/L3-DFA2-revised.pdf) which might be better since it also makes sense for backward analysis.

Reviewed By: Mogball

Differential Revision: https://reviews.llvm.org/D132086