Optimize fft(0) to be 0

```mlir
p.tmpVec.mlir_data = %12 = "stablehlo.broadcast_in_dim"(%11) <{broadcast_dimensions = array}> : (tensor>) -> tensor<12x12xcomplex>
p.forwardFFT = ReactantAbstractFFTsExt.ReactantFFTInPlacePlan{ComplexF64, UnitRange{Int64}}(1:2)
p.tmpVec.mlir_data = %89 = "stablehlo.transpose"(%87) <{permutation = array}> : (tensor<12x12xcomplex>) -> tensor<12x12xcomplex>
module @reactant_nfft attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
func.func @main(%arg0: tensor<6x6xcomplex> {enzymexla.memory_effects = []}) -> tensor<6xcomplex> attributes {enzymexla.memory_effects = []} {
%cst = stablehlo.constant 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: tensor<6x144xcomplex>
%cst_0 = stablehlo.constant dense<(0.000000e+00,0.000000e+00)> : tensor<12x12xcomplex>
%0 = stablehlo.fft %cst_0, type = FFT, length = [12, 12] : (tensor<12x12xcomplex>) -> tensor<12x12xcomplex>
%1 = stablehlo.reshape %0 : (tensor<12x12xcomplex>) -> tensor<144x1xcomplex>
%2 = stablehlo.dot_general %cst, %1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<6x144xcomplex>, tensor<144x1xcomplex>) -> tensor<6x1xcomplex>
%3 = stablehlo.reshape %2 : (tensor<6x1xcomplex>) -> tensor<6xcomplex>
return %3 : tensor<6xcomplex>
}
}
```