## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of cereal' and 'pickles', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $1.79x_1 + 4.7x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $3x_1 + 3x_2 \geq 37$ (at least 37 grams of carbohydrates)
- $6x_1 + 2x_2 \geq 11$ (total combined sourness index minimum)
- $10x_1 - 6x_2 \geq 0$ ( constraint on bowls of cereal and pickles)
- $3x_1 + 3x_2 \leq 46$ (no more than 46 grams of carbohydrates)
- $6x_1 + 2x_2 \leq 39$ (total combined sourness index maximum)

## 4: Define the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'bowls of cereal'), ('x2', 'pickles')],
'objective_function': '1.79*x1 + 4.7*x2',
'constraints': [
    '3*x1 + 3*x2 >= 37',
    '6*x1 + 2*x2 >= 11',
    '10*x1 - 6*x2 >= 0',
    '3*x1 + 3*x2 <= 46',
    '6*x1 + 2*x2 <= 39'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="bowls_of_cereal", lb=0)  # bowls of cereal
    x2 = model.addVar(name="pickles", lb=0)  # pickles

    # Define the objective function
    model.setObjective(1.79 * x1 + 4.7 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + 3 * x2 >= 37, name="carbohydrates_min")
    model.addConstr(6 * x1 + 2 * x2 >= 11, name="sourness_min")
    model.addConstr(10 * x1 - 6 * x2 >= 0, name="bowls_pickles_constraint")
    model.addConstr(3 * x1 + 3 * x2 <= 46, name="carbohydrates_max")
    model.addConstr(6 * x1 + 2 * x2 <= 39, name="sourness_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"bowls of cereal: {x1.varValue}")
        print(f"pickles: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```