## Step 1: Define the symbolic representation of the problem
Let's denote:
- $x_1$ as the number of premium seats,
- $x_2$ as the number of regular seats.

The objective is to maximize profit, where the profit from premium seats is $150x_1$ and from regular seats is $50x_2$. Therefore, the objective function is $150x_1 + 50x_2$.

## Step 2: List the constraints
1. Total seats: $x_1 + x_2 \leq 300$ (since there are 300 seats in total),
2. Minimum premium seats: $x_1 \geq 50$ (at least 50 seats will be assigned as premium seats),
3. Preference for regular seats: $x_2 \geq 3x_1$ (at least 3 times as many people prefer the regular seats to the premium seats),
4. Non-negativity: $x_1 \geq 0$ and $x_2 \geq 0$ (number of seats cannot be negative).

## 3: Symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'premium seats'), ('x2', 'regular seats')],
'objective_function': '150*x1 + 50*x2',
'constraints': [
    'x1 + x2 <= 300',
    'x1 >= 50',
    'x2 >= 3*x1',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Convert the problem into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="premium_seats", lb=0, ub=300, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="regular_seats", lb=0, ub=300, vtype=gurobi.GRB.INTEGER)

    # Objective function: Maximize 150*x1 + 50*x2
    model.setObjective(150*x1 + 50*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 300, name="total_seats")
    model.addConstr(x1 >= 50, name="min_premium_seats")
    model.addConstr(x2 >= 3*x1, name="regular_vs_premium")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution found. Maximum profit: ${model.objVal:.2f}")
        print(f"Number of premium seats to sell: {x1.x:.0f}")
        print(f"Number of regular seats to sell: {x2.x:.0f}")
    else:
        print("No optimal solution found.")

solve_concert_seating_problem()
```