To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints.

Let's define:
- $x_1$ as the number of premium class seats sold,
- $x_2$ as the number of regular class seats sold.

The objective is to maximize profit, which can be represented by the objective function: $50x_1 + 30x_2$.

Now, let's formulate the constraints based on the problem description:
1. The bus carries at most 100 passengers: $x_1 + x_2 \leq 100$.
2. At least 30 seats are reserved for premium class: $x_1 \geq 30$.
3. At least twice as many passengers prefer regular class over premium class: $x_2 \geq 2x_1$.

Given this, the symbolic representation of our problem in JSON format is:
```json
{
    'sym_variables': [('x1', 'number of premium class seats sold'), ('x2', 'number of regular class seats sold')],
    'objective_function': '50*x1 + 30*x2',
    'constraints': ['x1 + x2 <= 100', 'x1 >= 30', 'x2 >= 2*x1']
}
```

Now, let's implement this problem in Gurobi using Python. We'll use the `gurobipy` library to formulate and solve the linear programming problem.

```python
from gurobipy import *

# Create a new model
m = Model("Bus_Seating_Optimization")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="premium_class_seats")
x2 = m.addVar(vtype=GRB.INTEGER, name="regular_class_seats")

# Set objective function: Maximize profit
m.setObjective(50*x1 + 30*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 100, "total_passengers")
m.addConstr(x1 >= 30, "premium_class_minimum")
m.addConstr(x2 >= 2*x1, "regular_class_preferred")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Premium class seats: {x1.x}")
    print(f"Regular class seats: {x2.x}")
    print(f"Maximum profit: ${50*x1.x + 30*x2.x}")
else:
    print("No optimal solution found.")
```