## Step 1: Define the symbolic representation of the problem
Let's denote:
- $x_1$ as the number of first-floor seats sold
- $x_2$ as the number of second-floor seats sold

The objective is to maximize profit, where first-floor seats make a profit of $100 each and second-floor seats make a profit of $70 each. So, the objective function is $100x_1 + 70x_2$.

## Step 2: List the constraints based on the problem description
1. Total seats: $x_1 + x_2 \leq 250$ (since there are 250 seats in total)
2. Minimum first-floor seats: $x_1 \geq 70$ (at least 70 seats will be assigned as first-floor seats)
3. Preference for second-floor seats: $x_2 \geq 2x_1$ (at least 2 times as many people prefer the second-floor seats to the first-floor seats)
4. Non-negativity: $x_1 \geq 0, x_2 \geq 0$ (number of seats cannot be negative)

## 3: Write down the symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'first-floor seats'), ('x2', 'second-floor seats')],
'objective_function': '100*x1 + 70*x2',
'constraints': [
    'x1 + x2 <= 250',
    'x1 >= 70',
    'x2 >= 2*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(lb=0, name="first_floor_seats")
    x2 = model.addVar(lb=0, name="second_floor_seats")

    # Objective function: maximize 100*x1 + 70*x2
    model.setObjective(100*x1 + 70*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 250, name="total_seats")
    model.addConstr(x1 >= 70, name="min_first_floor")
    model.addConstr(x2 >= 2*x1, name="second_floor_preference")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of first-floor seats: {x1.varValue}")
        print(f"Number of second-floor seats: {x2.varValue}")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_concert_seating_problem()
```