## Step 1: Define the symbolic representation of the problem
Let's denote the number of regular rate tickets as $x_1$ and the number of concession rate tickets as $x_2$. The objective is to maximize profit, which is $2x_1 + 0.5x_2$. The constraints are:
- The ferry can carry at most 120 people: $x_1 + x_2 \leq 120$
- At least 15 tickets are reserved for concession rate: $x_2 \geq 15$
- At least 5 times as many tickets sold are regular rate tickets than concession rate tickets: $x_1 \geq 5x_2$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```json
{
'sym_variables': [('x1', 'regular rate tickets'), ('x2', 'concession rate tickets')],
'objective_function': '2*x1 + 0.5*x2',
'constraints': [
    'x1 + x2 <= 120',
    'x2 >= 15',
    'x1 >= 5*x2',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 3: Translate the symbolic representation into Gurobi code
Now, let's translate this into Gurobi code in Python:

```python
import gurobi

def solve_ferry_ticket_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="regular_rate_tickets", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="concession_rate_tickets", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: maximize 2*x1 + 0.5*x2
    model.setObjective(2*x1 + 0.5*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 120, name="total_capacity")
    model.addConstr(x2 >= 15, name="concession_rate_min")
    model.addConstr(x1 >= 5*x2, name="regular_to_concession_rate")

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Regular rate tickets: {x1.varValue}")
        print(f"Concession rate tickets: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_ferry_ticket_problem()
```