## Step 1: Define the symbolic representation of the problem
Let's denote the number of premium seats as $x_1$ and the number of regular seats as $x_2$. The profit made on each premium seat is $40, and on each regular seat is $20. The objective is to maximize the total profit.

## Step 2: Formulate the objective function
The objective function to maximize profit is $40x_1 + 20x_2$.

## 3: Identify the constraints
1. The tour bus has 100 seats: $x_1 + x_2 \leq 100$.
2. At least 10 seats are reserved to be premium: $x_1 \geq 10$.
3. At least 5 times as many people prefer regular seats to premium seats: $x_2 \geq 5x_1$.

## 4: Symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'premium seats'), ('x2', 'regular seats')],
    'objective_function': '40*x1 + 20*x2',
    'constraints': [
        'x1 + x2 <= 100',
        'x1 >= 10',
        'x2 >= 5*x1'
    ]
}
```

## 5: Convert the problem into Gurobi code
We will use the Gurobi Python API to solve this linear programming problem.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='premium_seats', lb=0, ub=100, vtype=gurobi.GRB.INTEGER)  # Number of premium seats
    x2 = model.addVar(name='regular_seats', lb=0, ub=100, vtype=gurobi.GRB.INTEGER)  # Number of regular seats

    # Objective function: Maximize 40*x1 + 20*x2
    model.setObjective(40*x1 + 20*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 100, name='total_seats')  # Total seats do not exceed 100
    model.addConstr(x1 >= 10, name='min_premium_seats')  # At least 10 premium seats
    model.addConstr(x2 >= 5*x1, name='regular_vs_premium')  # At least 5 times as many regular seats as premium seats

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {model.x[0]} premium seats, {model.x[1]} regular seats")
        print(f"Maximum profit: ${40*model.x[0] + 20*model.x[1]}")
    else:
        print("No optimal solution found")

solve_tour_bus_seating()
```