To convert the given problem description into a symbolic representation and subsequently into Gurobi code, we first need to identify the variables, objective function, and constraints based on the provided information.

### Symbolic Representation

- **Variables**:
  - $x_1$: milligrams of potassium
  - $x_2$: milligrams of vitamin B2

- **Objective Function**:
  The goal is to minimize $6x_1 + 2x_2$.

- **Constraints**:
  1. Immune support index from potassium: $26x_1$
  2. Kidney support index from potassium: $25x_1$
  3. Immune support index from vitamin B2: $7x_2$
  4. Kidney support index from vitamin B2: $21x_2$
  5. Minimum combined immune support index: $26x_1 + 7x_2 \geq 19$
  6. Minimum combined kidney support index: $25x_1 + 21x_2 \geq 22$
  7. Constraint on potassium and vitamin B2: $-6x_1 + 6x_2 \geq 0$
  8. Maximum combined immune support index: $26x_1 + 7x_2 \leq 48$
  9. Maximum combined kidney support index: $25x_1 + 21x_2 \leq 45$

Given the above, the symbolic representation in JSON format 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'
  ]
}
```

### Gurobi Code

To solve this optimization problem using Gurobi, we will use Python as the programming language. The code snippet below sets up and solves the linear program based on the symbolic representation provided.

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(lb=0, name="milligrams_of_potassium")
x2 = m.addVar(lb=0, name="milligrams_of_vitamin_B2")

# Set the objective function
m.setObjective(6*x1 + 2*x2, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(26*x1 + 7*x2 >= 19, name="min_combined_immune_support")
m.addConstr(25*x1 + 21*x2 >= 22, name="min_combined_kidney_support")
m.addConstr(-6*x1 + 6*x2 >= 0, name="potassium_vitamin_B2_constraint")
m.addConstr(26*x1 + 7*x2 <= 48, name="max_combined_immune_support")
m.addConstr(25*x1 + 21*x2 <= 45, name="max_combined_kidney_support")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Potassium: {x1.x}")
    print(f"Milligrams of Vitamin B2: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```