To solve 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 define:
- $x_1$ as the milligrams of potassium,
- $x_2$ as the milligrams of vitamin B5.

The objective function to minimize is: $2.82x_1 + 7.0x_2$

The constraints are:
1. $6x_1 \geq 82$ (Digestive support index for milligrams of potassium),
2. $13x_2 \geq 82$ (Digestive support index for milligrams of vitamin B5),
3. $6x_1 + 13x_2 \geq 82$ (Total combined digestive support index from both),
4. $6x_1 + 13x_2 \geq 82$ (This constraint is identical to the third one and seems redundant),
5. $-5x_1 + x_2 \geq 0$,
6. $6x_1 + 13x_2 \leq 164$.

Given that $x_1$ and $x_2$ can be non-integer (fractional amounts are allowed), we treat them as continuous variables.

Here is the symbolic representation of the problem:
```json
{
    'sym_variables': [('x1', 'milligrams of potassium'), ('x2', 'milligrams of vitamin B5')],
    'objective_function': '2.82*x1 + 7.0*x2',
    'constraints': [
        '6*x1 >= 82',
        '13*x2 >= 82',
        '6*x1 + 13*x2 >= 82',
        '-5*x1 + x2 >= 0',
        '6*x1 + 13*x2 <= 164'
    ]
}
```

And here is the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_potassium")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B5")

# Set the objective function
m.setObjective(2.82*x1 + 7.0*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(6*x1 >= 82, "digestive_support_potassium")
m.addConstr(13*x2 >= 82, "digestive_support_vitamin_B5")
m.addConstr(6*x1 + 13*x2 >= 82, "total_digestive_support_min")
m.addConstr(-5*x1 + x2 >= 0, "potassium_vitamin_b5_ratio")
m.addConstr(6*x1 + 13*x2 <= 164, "total_digestive_support_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Potassium: {x1.x}")
    print(f"Milligrams of Vitamin B5: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```