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

Let's denote:
- $x_1$ as the number of small bowls sold
- $x_2$ as the number of large bowls sold

The objective is to maximize profit, where the profit per small bowl is $5 and per large bowl is $8. So, the objective function is $5x_1 + 8x_2$.

## Step 2: Define the constraints based on the available fruits

The small bowl contains 3 units of kiwi, 2 units of mango, and 5 units of pineapple. The large bowl contains 5 units of kiwi, 4 units of mango, and 8 units of pineapple. The store has available 100 units of kiwi, 120 units of mango, and 150 units of pineapple.

Thus, the constraints are:
- $3x_1 + 5x_2 \leq 100$ (kiwi constraint)
- $2x_1 + 4x_2 \leq 120$ (mango constraint)
- $5x_1 + 8x_2 \leq 150$ (pineapple constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of bowls cannot be negative)

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'small bowls'), ('x2', 'large bowls')],
    'objective_function': '5*x1 + 8*x2',
    'constraints': [
        '3*x1 + 5*x2 <= 100',
        '2*x1 + 4*x2 <= 120',
        '5*x1 + 8*x2 <= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="small_bowls")
    x2 = model.addVar(lb=0, name="large_bowls")

    # Define the objective function
    model.setObjective(5 * x1 + 8 * x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(3 * x1 + 5 * x2 <= 100, name="kiwi_constraint")
    model.addConstr(2 * x1 + 4 * x2 <= 120, name="mango_constraint")
    model.addConstr(5 * x1 + 8 * x2 <= 150, name="pineapple_constraint")

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```