## Step 1: Define the symbolic representation of the variables
The variables are 'protein bars' and 'ham sandwiches', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $5.24x_1 + 6.19x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $26x_1 + 3x_2 \geq 47$
2. $25x_1 + 16x_2 \geq 66$
3. $x_1 - 7x_2 \geq 0$
4. $26x_1 + 3x_2 \leq 72$
5. $25x_1 + 16x_2 \leq 121$
6. $x_1, x_2 \geq 0$ and are integers.

## 4: Correct and Simplify Constraints for Accuracy
Upon review, constraints 4 and 5 have upper bounds that were mentioned in the problem description as 'no more than 72' and 'up to 121 milligrams of iron' respectively, but specifically stated as 72 or less and 121 milligrams. These are already captured. However, we note that:
- The umami index upper bound is 112, but a specific constraint of 72 or less is given, which seems to override or be a specific case.
- The iron upper bound is given as 136 but specifically mentioned as 121.

## 5: Adjust Constraints Based on Given Resources/Attributes
Given resources/attributes:
- $r_0$: umami index with $x_0 = 26$, $x_1 = 3$, and upper bound $112$.
- $r_1$: milligrams of iron with $x_0 = 25$, $x_1 = 16$, and upper bound $136$.

## 6: Formulate the Problem Symbolically
The symbolic representation is:
- Variables: $x_1$ (protein bars), $x_2$ (ham sandwiches)
- Objective function: Maximize $5.24x_1 + 6.19x_2$
- Constraints:
1. $26x_1 + 3x_2 \geq 47$
2. $25x_1 + 16x_2 \geq 66$
3. $x_1 - 7x_2 \geq 0$
4. $26x_1 + 3x_2 \leq 72$
5. $25x_1 + 16x_2 \leq 121$
6. $x_1, x_2 \geq 0$ and are integers.

## 7: Convert to Gurobi Code
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="protein_bars", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="ham_sandwiches", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(5.24 * x1 + 6.19 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(26 * x1 + 3 * x2 >= 47, name="umami_index_min")
    model.addConstr(25 * x1 + 16 * x2 >= 66, name="iron_min")
    model.addConstr(x1 - 7 * x2 >= 0, name="protein_bars_vs_ham_sandwiches")
    model.addConstr(26 * x1 + 3 * x2 <= 72, name="umami_index_max")
    model.addConstr(25 * x1 + 16 * x2 <= 121, name="iron_max")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Protein bars: {x1.varValue}")
        print(f"Ham sandwiches: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 8: Symbolic Representation Output
```json
{
    'sym_variables': [('x1', 'protein bars'), ('x2', 'ham sandwiches')],
    'objective_function': '5.24*x1 + 6.19*x2',
    'constraints': [
        '26*x1 + 3*x2 >= 47',
        '25*x1 + 16*x2 >= 66',
        'x1 - 7*x2 >= 0',
        '26*x1 + 3*x2 <= 72',
        '25*x1 + 16*x2 <= 121'
    ]
}
```