## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of potassium' and 'milligrams of vitamin B2'. Let's denote 'milligrams of potassium' as $x_1$ and 'milligrams of vitamin B2' as $x_2$. The objective function to minimize is $6x_1 + 2x_2$.

## 2: List the constraints
The constraints given are:
1. $26x_1 + 7x_2 \geq 19$
2. $25x_1 + 21x_2 \geq 22$
3. $-6x_1 + 6x_2 \geq 0$
4. $26x_1 + 7x_2 \leq 48$
5. $25x_1 + 21x_2 \leq 45$

## 3: Symbolic representation
The symbolic representation of the problem is:
- Variables: $x_1$ for 'milligrams of potassium', $x_2$ for 'milligrams of vitamin B2'
- Objective function: $6x_1 + 2x_2$
- Constraints:
  1. $26x_1 + 7x_2 \geq 19$
  2. $25x_1 + 21x_2 \geq 22$
  3. $-6x_1 + 6x_2 \geq 0$
  4. $26x_1 + 7x_2 \leq 48$
  5. $25x_1 + 21x_2 \leq 45$

## 4: Gurobi Code
To solve this problem using Gurobi, we will use Python. The Gurobi code for this problem is as follows:

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="milligrams_of_potassium", lb=0)  # lower bound is 0
    x2 = model.addVar(name="milligrams_of_vitamin_B2", lb=0)  # lower bound is 0

    # Objective function: minimize 6 * x1 + 2 * x2
    model.setObjective(6 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(26 * x1 + 7 * x2 >= 19, name="immune_support_index_min")
    model.addConstr(25 * x1 + 21 * x2 >= 22, name="kidney_support_index_min")
    model.addConstr(-6 * x1 + 6 * x2 >= 0, name="balance_constraint")
    model.addConstr(26 * x1 + 7 * x2 <= 48, name="immune_support_index_max")
    model.addConstr(25 * x1 + 21 * x2 <= 45, name="kidney_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of potassium: {x1.varValue}")
        print(f"Milligrams of vitamin B2: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 5: JSON Representation
The JSON representation of the problem is:

```json
{
    'sym_variables': [('x1', 'milligrams of potassium'), ('x2', 'milligrams of vitamin B2')],
    'objective_function': '6*x1 + 2*x2',
    'constraints': [
        '26*x1 + 7*x2 >= 19',
        '25*x1 + 21*x2 >= 22',
        '-6*x1 + 6*x2 >= 0',
        '26*x1 + 7*x2 <= 48',
        '25*x1 + 21*x2 <= 45'
    ]
}
```