To address the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using mathematical notation.

Let's denote:
- $x_1$ as the number of grams of fiber,
- $x_2$ as the number of milligrams of potassium.

The objective function to be minimized is given by:
\[8.96x_1^2 + 5.67x_1 + 2.38x_2\]

The constraints are as follows:
1. The energy stability index for grams of fiber is 4: This constraint isn't directly related to the optimization problem's objective or other constraints, so it seems more like a characteristic than a constraint.
2. The total combined energy stability index from grams of fiber squared plus milligrams of potassium squared must be as much or more than 24:
\[x_1^2 + x_2^2 \geq 24\]
3. The total combined energy stability index from grams of fiber, and milligrams of potassium should be 24 or more:
\[4x_1 + x_2 \geq 24\]
4. 9 times the number of grams of fiber squared, plus -8 times the number of milligrams of potassium squared has to be at least zero:
\[9x_1^2 - 8x_2^2 \geq 0\]
5. The total combined energy stability index from grams of fiber squared plus milligrams of potassium squared has to be equal to or less than 71:
\[x_1^2 + x_2^2 \leq 71\]

Given these definitions, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of potassium')],
    'objective_function': '8.96*x1**2 + 5.67*x1 + 2.38*x2',
    'constraints': [
        'x1**2 + x2**2 >= 24',
        '4*x1 + x2 >= 24',
        '9*x1**2 - 8*x2**2 >= 0',
        'x1**2 + x2**2 <= 71'
    ]
}
```

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=-GRB.INFINITY, ub=GRB.INFINITY, name="grams_of_fiber")
x2 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, name="milligrams_of_potassium")

# Set the objective function
m.setObjective(8.96*x1**2 + 5.67*x1 + 2.38*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1**2 + x2**2 >= 24, name="energy_stability_index_squared")
m.addConstr(4*x1 + x2 >= 24, name="total_energy_stability_index")
m.addConstr(9*x1**2 - 8*x2**2 >= 0, name="fiber_and_potassium_constraint")
m.addConstr(x1**2 + x2**2 <= 71, name="energy_stability_index_squared_upper_bound")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of fiber: {x1.x}")
    print(f"Milligrams of potassium: {x2.x}")
else:
    print("No optimal solution found.")
```