## Step 1: Define the symbolic representation of the problem
Let's denote the number of wraps to be sold as $x_1$ and the number of bowls to be sold as $x_2$. The objective is to maximize profit, which is $5x_1 + 7x_2$. The constraints are based on the availability of rice and fish. Each wrap contains 3 units of rice and each bowl contains 5 units of rice, with 800 units of rice available. This gives us the constraint $3x_1 + 5x_2 \leq 800$. Similarly, for fish, with 2 units per wrap, 3 units per bowl, and 700 units available, we have $2x_1 + 3x_2 \leq 700$. Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of wraps and bowls cannot be negative.

## 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for wraps
- $x_2$ for bowls

The objective function to maximize is: $5x_1 + 7x_2$

The constraints are:
- $3x_1 + 5x_2 \leq 800$ (rice constraint)
- $2x_1 + 3x_2 \leq 700$ (fish constraint)
- $x_1 \geq 0$ (non-negativity constraint for wraps)
- $x_2 \geq 0$ (non-negativity constraint for bowls)

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

## 3: Convert the problem into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="wraps", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="bowls", lb=0, ub=gurobi.GRB.INFINITY)

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

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

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of wraps to sell: {x1.varValue}")
        print(f"Number of bowls to sell: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```