## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of heated seats sold
- $x_2$ as the number of regular seats sold

The objective is to maximize profit, given that the profit per heated seat is $20 and the profit per regular seat is $15. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 20x_1 + 15x_2 \]

## Step 2: Identify the constraints

From the problem description, we have the following constraints:
1. The train has 100 seats available: $x_1 + x_2 \leq 100$
2. The train reserves at least 15 seats to be heated: $x_1 \geq 15$
3. At least 3 times as many people prefer regular seats to heated seats: $x_2 \geq 3x_1$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

However, since $x_1$ and $x_2$ represent the number of seats, they should naturally be non-negative, so we focus on the explicitly mentioned constraints.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'heated seats'), ('x2', 'regular seats')],
    'objective_function': '20*x1 + 15*x2',
    'constraints': [
        'x1 + x2 <= 100',
        'x1 >= 15',
        'x2 >= 3*x1'
    ]
}
```

## 4: Convert the problem into Gurobi code

Now, let's translate this into Gurobi code in Python:

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="heated_seats", lb=0, ub=None)
    x2 = model.addVar(name="regular_seats", lb=0, ub=None)

    # Objective function: Maximize 20*x1 + 15*x2
    model.setObjective(20*x1 + 15*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 100, name="total_seats")
    model.addConstr(x1 >= 15, name="min_heated_seats")
    model.addConstr(x2 >= 3*x1, name="regular_vs_heated")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Heated seats = {x1.varValue}, Regular seats = {x2.varValue}")
    else:
        print("No optimal solution found")

solve_train_seating_problem()
```