To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

### Symbolic Representation:

- **Variables:**
  - Let $x_1$ represent the amount of potatoes.
  - Let $x_2$ represent the amount of protein bars.

- **Objective Function:**
  The objective is to minimize $5x_1 + 2x_2$.

- **Constraints:**
  1. Healthiness rating constraint: $5x_1 + 7x_2 \geq 31$
  2. Tastiness rating constraint: $2x_1 + x_2 \geq 20$
  3. Fiber constraint: $8x_1 + 10x_2 \geq 24$
  4. Another healthiness rating constraint (redundant with the first but included for completeness): $5x_1 + 7x_2 = 31$ is not necessary as it contradicts the inequality; instead, we focus on the upper bound given later.
  5. Linear combination constraint: $9x_1 - x_2 \geq 0$
  6. Upper bound healthiness rating constraint: $5x_1 + 7x_2 \leq 52$
  7. Upper bound tastiness rating constraint: $2x_1 + x_2 \leq 50$
  8. Upper bound fiber constraint: $8x_1 + 10x_2 \leq 30$

Given these constraints and the objective function, we can now write the symbolic representation of the problem as follows:

```json
{
  'sym_variables': [('x1', 'potatoes'), ('x2', 'protein bars')],
  'objective_function': '5*x1 + 2*x2',
  'constraints': [
    '5*x1 + 7*x2 >= 31',
    '2*x1 + x2 >= 20',
    '8*x1 + 10*x2 >= 24',
    '9*x1 - x2 >= 0',
    '5*x1 + 7*x2 <= 52',
    '2*x1 + x2 <= 50',
    '8*x1 + 10*x2 <= 30'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="potatoes")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="protein_bars")

# Set the objective function
m.setObjective(5*x1 + 2*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x1 + 7*x2 >= 31, "healthiness_rating_constraint")
m.addConstr(2*x1 + x2 >= 20, "tastiness_rating_constraint")
m.addConstr(8*x1 + 10*x2 >= 24, "fiber_constraint")
m.addConstr(9*x1 - x2 >= 0, "linear_combination_constraint")
m.addConstr(5*x1 + 7*x2 <= 52, "upper_bound_healthiness_rating")
m.addConstr(2*x1 + x2 <= 50, "upper_bound_tastiness_rating")
m.addConstr(8*x1 + 10*x2 <= 30, "upper_bound_fiber")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Potatoes: {x1.x}")
    print(f"Protein Bars: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```