Enable parallel distribution for tensornet and tensornet-mps

Required prerequisites

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Describe the feature

Hamiltonian batching workflows do not work on the tensornet and tensornet-mps backend. We need to add a mqpu version of those I believe.

import cudaq
from cudaq import spin

# cudaq.set_target("nvidia", option="mqpu")
cudaq.set_target('tensornet')
# cudaq.set_target('tensornet-mps')

cudaq.mpi.initialize()

qubit_count = 15
term_count = 100

kernel = cudaq.make_kernel()
qubits = kernel.qalloc(qubit_count)
kernel.h(qubits[0])
for i in range(1, qubit_count):
    kernel.cx(qubits[0], qubits[i])

hamiltonian = cudaq.SpinOperator.random(qubit_count, term_count)

result = cudaq.observe(kernel, hamiltonian, execution=cudaq.parallel.thread).expectation()

cudaq.mpi.finalize()

RuntimeError: The current quantum_platform does not support parallel distribution of observe() expectation value computations.

Reproduce the failure with the Python example using cudaq.observe(), cudaq.parallel.thread, and the tensornet or tensornet-mps target. Trace how the current quantum_platform handles parallel distribution and compare it with the commented nvidia mqpu target. Done means Hamiltonian batching works on both targets without the reported RuntimeError, with coverage for the supported behavior.