To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the unknown quantities (number of wraps and bowls), formulating the objective function based on profit, and translating the constraints related to the availability of rice and fish into mathematical expressions.

Let's define:
- \(x_1\) as the number of wraps sold,
- \(x_2\) as the number of bowls sold.

The symbolic representation of the problem can be described as follows:

```json
{
  'sym_variables': [('x1', 'number of wraps'), ('x2', 'number of bowls')],
  'objective_function': '5*x1 + 7*x2',
  'constraints': ['3*x1 + 5*x2 <= 800', '2*x1 + 3*x2 <= 700', 'x1 >= 0', 'x2 >= 0']
}
```

This representation captures the essence of the problem: maximizing profit (objective function) by selling wraps and bowls (variables), subject to constraints on rice and fish availability, and ensuring that the quantities sold are non-negative.

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="wraps")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bowls")

# Set the objective function
m.setObjective(5*x1 + 7*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 5*x2 <= 800, "rice_constraint")
m.addConstr(2*x1 + 3*x2 <= 700, "fish_constraint")

# Optimize the model
m.optimize()

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