Modular quantum computing provides a scalable approach to overcome the limitations of monolithic quantum architectures by interconnecting multiple Quantum Processing Units (QPUs) through a quantum network. In this work, we explore and evaluate two entanglement scheduling strategies-static and dynamic-and analyze their performance in terms of circuit execution delay and network resource utilization under realistic assumptions and practical limitations such as probabilistic entanglement generation, limited communication qubits, photonic switch reconfiguration delays, and topology-induced contention. We show that dynamic scheduling consistently outperforms static scheduling in scenarios with high entanglement parallelism, especially when network resources are scarce. Furthermore, we investigate the impact of communication qubit coherence time, modeled as a cutoff for holding EPR pairs, and demonstrate that aggressive lookahead strategies can degrade performance when coherence times are short, due to premature entanglement discarding and wasted resources. We also identify congestion-free BSM provisioning by profiling peak BSM usage per switch. Our results provide actionable insights for scheduler design and resource provisioning in realistic quantum data centers, bringing system-level considerations closer to practical quantum computing deployment.