Personalized long-form question answering aims to tailor responses based on a user's historical data, which often spans diverse information domains. While prior work typically assumes user profiles are domain-aligned with the users' questions, real-world profiles are often diverse, containing information across multiple domains. In this work, we use the LaMP-QA benchmark, where user profiles span diverse Stack Exchange domains (Arts & Entertainment, Lifestyle & Personal Development, and Society & Culture), to systematically investigate how domain composition of the user profile affects personalization performance. We examine three profile composition settings: Cross-Domain, where profiles contain only out-of-domain information; Multi-Domain, where profiles span multiple domains including the question's domain; and In-Domain, where profiles match the question domain. Our analysis reveals that state-of-the-art personalization methods struggle in Cross-Domain settings, often failing to outperform non-personalized baselines. To address this challenge, we introduce Dual Retrieval Augmentation, a framework that jointly retrieves from both the user profile and a public corpus. We apply our framework to existing personalization approaches and demonstrate that it consistently outperforms state-of-the-art methods across all three profile settings, validating its robustness for personalized question answering.