## Step 1: Define the symbolic representation of the variables
The variables are 'hamburgers' and 'fruit salads', 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 minimize is $5.28x_1 + 7.67x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $4x_1 + 8x_2 \geq 47$ (at least 47 grams of fat must come from hamburgers plus fruit salads)
- $4x_1 \geq 47$ (at least 47 grams of fat must come from hamburgers)
- $x_1 + 3x_2 \geq 26$ (total combined sourness index from hamburgers plus fruit salads has to be equal to or greater than 26)
- $10x_1 - 5x_2 \geq 0$ (ten times the number of hamburgers, plus -5 times the number of fruit salads should be greater than or equal to zero)
- $4x_1 + 8x_2 \leq 93$ (at most 93 grams of fat from hamburgers and fruit salads)
- $x_1 + 3x_2 \leq 68$ (total combined sourness index from hamburgers, and fruit salads has to be 68 or less)

## 4: Define the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'hamburgers'), ('x2', 'fruit salads')],
'objective_function': '5.28*x1 + 7.67*x2',
'constraints': [
    '4*x1 + 8*x2 >= 47',
    '4*x1 >= 47',
    'x1 + 3*x2 >= 26',
    '10*x1 - 5*x2 >= 0',
    '4*x1 + 8*x2 <= 93',
    'x1 + 3*x2 <= 68'
]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    hamburgers = model.addVar(name="hamburgers", lb=0)
    fruit_salads = model.addVar(name="fruit_salads", lb=0)

    # Define the objective function
    model.setObjective(5.28 * hamburgers + 7.67 * fruit_salads, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(4 * hamburgers + 8 * fruit_salads >= 47, name="fat_from_both")
    model.addConstr(4 * hamburgers >= 47, name="fat_from_hamburgers")
    model.addConstr(hamburgers + 3 * fruit_salads >= 26, name="sourness_index")
    model.addConstr(10 * hamburgers - 5 * fruit_salads >= 0, name="hamburgers_vs_fruit_salads")
    model.addConstr(4 * hamburgers + 8 * fruit_salads <= 93, name="max_fat_from_both")
    model.addConstr(hamburgers + 3 * fruit_salads <= 68, name="max_sourness_index")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Hamburgers: {hamburgers.varValue}")
        print(f"Fruit Salads: {fruit_salads.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```