## Step 1: Define the symbolic representation of the problem
Let's denote the number of breakfast options as $x_1$ and the number of lunch options as $x_2$. The profit per breakfast option is $10, and the profit per lunch option is $8. The preparation time for breakfast is 7 minutes and for lunch is 8 minutes, with 700 minutes available for preparation. The packaging time for breakfast is 2 minutes and for lunch is 3 minutes, with 500 minutes available for packaging.

## Step 2: Formulate the objective function
The objective function to maximize profit is $10x_1 + 8x_2$.

## 3: Define the constraints
The constraints based on the given resources are:
1. Preparation time constraint: $7x_1 + 8x_2 \leq 700$
2. Packaging time constraint: $2x_1 + 3x_2 \leq 500$
Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of meals cannot be negative.

## 4: Symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'breakfast option'), ('x2', 'lunch option')],
'objective_function': '10*x1 + 8*x2',
'constraints': ['7*x1 + 8*x2 <= 700', '2*x1 + 3*x2 <= 500', 'x1 >= 0', 'x2 >= 0']
}
```

## 5: Translate into Gurobi code
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="breakfast_option")
    x2 = model.addVar(lb=0, name="lunch_option")

    # Objective function
    model.setObjective(10*x1 + 8*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(7*x1 + 8*x2 <= 700, name="preparation_time_constraint")
    model.addConstr(2*x1 + 3*x2 <= 500, name="packaging_time_constraint")

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```