So I think the Erlang distinction is a little unrelated (I mean, it's technically accurate, but not because of the error recovery; that's an implementation detail. The original Actor formulation Carl Hewitt proposed was heavily influenced by Smalltalk semantics; whereas Smalltalk was truly OO, in that everything is an Object that passed messages, one issue it had was that objects did not equate to threads of execution. The Actor model, then, said each object executed independently. But in the same way that Smalltalk did not have primitives; even integers are always Objects, the Actor model stipulates there are no primitives; even integers are Actors). But I'll take a stab at answering what I think you're asking.

If your services don't deal with internal mutable state, nor high degrees of concurrency then there isn't much gain to be had with an actor system. That said, that begs the question of what the queue is for; just create more instances, since there's no internal state to share.

As soon as you start having internal mutable state and high levels of concurrency, that's where the actor model applies. Queues don't exist for concurrency (you don't need them; just create more executors), they exist for imposing sequence where it is needed (an obvious case; you have a DB connection, you want to only have one query at a time. So every desired query goes into a queue, and the process at the end that owns the DB connection pulls from it). Internal mutable state gets stored inside of an actor; updates and reads get serialized on that actor.

At the highest level, I would describe the actor model as taking a 'successful' model for distributed computing, and making it the only model you use, even locally.

In, let's say Java, for instance, using standard concurrency approaches, it matters where a process lives. My way of operating/communicating to another thread of execution (unit of concurrency) is very, very different than my way of operating/communicating to another machine. Locally I have threads and locks and need to be very mindful. When communicating to another machine, I send a message and that's it (maybe I expect a response, and timeout if I don't get one, but that's really just the same thing, the other machine sending a message).

I don't actually need a queue involved for theoretical correctness unless I need to process messages in sequence (after all, I could have multiple copies of the other process, and send a message to each of them). Now, in the real world I do, simply because if my concurrency gets too large it can't be handled by what the units of concurrency already available (instances, threads, whatever), and scale up takes time, but that's really just a special case of why I need to impose a sequence on messages (handle these first, then handle these, rather than handle all of them concurrently).

The actor model makes this the local model of communication (and so makes the impedance mismatch negligible between local and distributed; so much so that some languages it's actually irrelevant whether you're sending a message to a local actor, or a remote one). Scaling concurrency up internally just means spin up a new actor. When you need to serialize, you send messages to the same actor, where it ends up in a queue.

So it's not solving a different problem, exactly, if that problem is "how do we write systems that can do multiple things at once", but the specifics, complexity, etc, tend to be pretty different. The problems it's solving are a bit more subjective than simply "can we handle this problem", and more "how well do our tools and mental model lend themselves to the problem we're trying to solve".