Federated graph learning (FGL) is a distributive framework for graph representation learning that prioritizes privacy preservation. The right to be forgotten embodies the ethical principle of prioritizing user autonomy over data usage. In the context of FGL, upholding this right requires the method to remove specific entities and their associated knowledge within local subgraphs (Meta Unlearning) and the complete erasure of the entire client (Client Unlearning). We are the first to systematically define the above two unlearn requests in federated graph unlearning. Several studies have attempted to address this challenge, but key limitations persist: incomplete unlearning support and residual knowledge permeation. To this end, we propose a \textbf{P}rototype-guided \textbf{A}dversarial \textbf{G}raph \textbf{E}raser for universal federated graph unlearning (\textbf{PAGE}), the first unified federated graph unlearning framework that extend to comprehensive unlearning requests. For meta unlearning, we employ the prototype gradients guide initial local unlearn, while adversarial graphs eliminate residual knowledge across the influenced clients. For client unlearning, PAGE exclusively utilizes adversarial graph generation to purge a departed client's influence from the remaining participants. PAGE outperforms existing methods on 8 benchmark datasets. It improves prediction accuracy by 5.08\% (client unlearn) and 1.50\% (meta-unlearn), with up to 11.84\% gain on large-scale graphs. Furthermore, ablation studies confirm its efficacy as a plug-in for other meta unlearn methods, boosting prediction performance up to 4.49\% and unlearning performance up to 7.22\%.