To solve the optimization problem described, 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 breakfast options made,
- $x_2$ as the number of lunch options made.

The profit per breakfast option is $10, and the profit per lunch option is $8. Therefore, the objective function to maximize profit can be represented as:
\[ \text{Maximize:} \quad 10x_1 + 8x_2 \]

The constraints are based on the time available for preparations and packaging:
- Each breakfast option takes 7 minutes to prepare, so $x_1$ breakfast options take $7x_1$ minutes.
- Each lunch option takes 8 minutes to prepare, so $x_2$ lunch options take $8x_2$ minutes.
- The total preparation time available is 700 minutes. This gives us the constraint: $7x_1 + 8x_2 \leq 700$.
- Similarly, for packaging:
  - Each breakfast option takes 2 minutes to package, so $x_1$ breakfast options take $2x_1$ minutes.
  - Each lunch option takes 3 minutes to package, so $x_2$ lunch options take $3x_2$ minutes.
  - The total packaging time available is 500 minutes. This gives us the constraint: $2x_1 + 3x_2 \leq 500$.

Additionally, we have non-negativity constraints since the number of meals cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

In symbolic notation with natural language descriptions, our problem can be represented as:
```json
{
  'sym_variables': [('x1', 'number of breakfast options'), ('x2', 'number of lunch options')],
  'objective_function': '10*x1 + 8*x2',
  'constraints': ['7*x1 + 8*x2 <= 700', '2*x1 + 3*x2 <= 500', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="breakfast_options")
x2 = m.addVar(lb=0, vtype=GRB.INTEGER, name="lunch_options")

# Set the objective function
m.setObjective(10*x1 + 8*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 8*x2 <= 700, "preparation_time")
m.addConstr(2*x1 + 3*x2 <= 500, "packaging_time")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of breakfast options: {x1.x}")
    print(f"Number of lunch options: {x2.x}")
    print(f"Maximum profit: ${m.objVal}")
else:
    print("No optimal solution found")
```