To solve the given optimization problem, we need to first translate the natural language description into a symbolic representation. This involves defining variables for the objects in question (hamburgers and fruit salads), formulating the objective function using these variables, and then expressing the constraints in terms of these variables.

Let's define:
- $x_1$ as the number of hamburgers
- $x_2$ as the number of fruit salads

The objective function is to minimize: $5.28x_1 + 7.67x_2$

Given the constraints:
1. Fat content from hamburgers and fruit salads must be at least 47 grams: $4x_1 + 8x_2 \geq 47$
2. The total combined sourness index from hamburgers and fruit salads must be at least 26: $x_1 + 3x_2 \geq 26$
3. Ten times the number of hamburgers minus five times the number of fruit salads should be greater than or equal to zero: $10x_1 - 5x_2 \geq 0$
4. The total fat content from hamburgers and fruit salads must not exceed 93 grams: $4x_1 + 8x_2 \leq 93$
5. The total combined sourness index from hamburgers and fruit salads should not exceed 68: $x_1 + 3x_2 \leq 68$

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

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

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="hamburgers")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fruit_salads")

# Set the objective function
m.setObjective(5.28*x1 + 7.67*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + 8*x2 >= 47, "fat_content_min")
m.addConstr(x1 + 3*x2 >= 26, "sourness_index_min")
m.addConstr(10*x1 - 5*x2 >= 0, "hamburgers_vs_fruit_salads")
m.addConstr(4*x1 + 8*x2 <= 93, "fat_content_max")
m.addConstr(x1 + 3*x2 <= 68, "sourness_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"hamburgers: {x1.x}")
    print(f"fruit_salads: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```