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

Let's denote the number of burgers as $x_1$ and the number of orders of fries as $x_2$. The objective is to minimize the cost, which is $7x_1 + 3x_2$. The constraints are:

- Calorie requirement: $500x_1 + 300x_2 \geq 3000$
- Protein requirement: $30x_1 + 5x_2 \geq 150$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

The symbolic representation of the variables and the problem is as follows:
```json
{
    'sym_variables': [('x1', 'burgers'), ('x2', 'orders of fries')],
    'objective_function': '7*x1 + 3*x2',
    'constraints': [
        '500*x1 + 300*x2 >= 3000',
        '30*x1 + 5*x2 >= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 3: Write the Gurobi code to solve the problem

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # Number of burgers
    x2 = model.addVar(name='x2', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # Number of orders of fries

    # Define the objective function
    model.setObjective(7 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(500 * x1 + 300 * x2 >= 3000, name='calorie_requirement')
    model.addConstr(30 * x1 + 5 * x2 >= 150, name='protein_requirement')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The problem is infeasible")

solve_optimization_problem()
```