In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume the destination network acts as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network's average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. We demonstrate analytically and verify numerically that the optimal policy structure conforms to a threshold policy regarding the Version AoI at the aggregator. Furthermore, we establish that the optimal policy is independent of the Version AoI value at the destination nodes. Through numerical results, we elucidate the impact of system parameters on the average Version AoI of the network and the rationale behind this impact. Additionally, the simulations unveil scenarios wherein the performance of the optimal policy significantly surpasses that of a set of baseline policies.