Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, the presence of structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that inherent domain structural shift can be well reflected by the spectral nature of graphs. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies for effective global collaboration and personalized local application, we propose our pFGL framework FedSSP and further perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of FedSSP.