Apache Kafka (Option B) is the ideal choice for this scenario due to its robust features that align with the requirements:

1. Seek to a particular offset in a topic: Kafka allows consumers to jump to a specific offset within a topic, enabling them to read messages from that point forward. This capability is crucial for the requirement of seeking specific offsets.

2. Publish/subscribe on multiple topics: Designed for high-throughput and fault-tolerant real-time data streaming, Kafka efficiently handles the publish/subscribe model across numerous topics, meeting the need for multi-topic support.

3. Retain per-key ordering: Kafka ensures messages within the same partition are stored in the order they were received. This guarantees that messages with the same key maintain their order, fulfilling the per-key ordering requirement.

Why other options fall short:

• Dataflow (Option A): While excellent for stream and batch processing, Dataflow lacks Kafka's dedicated messaging and integration capabilities, especially in offset control and message ordering.
• Firebase Cloud Messaging (Option C): Primarily for mobile notifications, it doesn't support the data ingestion, retention, and ordering features needed here.
• Cloud Storage (Option D): Suitable for storing large data volumes but lacks real-time streaming and the specific messaging features required.

In conclusion, Apache Kafka stands out as the most suitable system for centralizing data ingestion and delivery in this context, thanks to its comprehensive messaging features, offset seeking, multi-topic publish/subscribe support, and per-key message ordering.