## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ = milligrams of potassium
- $x_2$ = milligrams of vitamin B12
- $x_3$ = milligrams of calcium
- $x_4$ = grams of fiber
- $x_5$ = milligrams of vitamin A

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is: $6.79x_1 + 6.61x_2 + 3.63x_3 + 6.11x_4 + 2.51x_5$

## 3: List all the constraints in symbolic notation
The constraints are:
1. $13x_1 + 9x_2 + 21x_3 + 10x_4 + 5x_5 \leq 164$ (kidney support index upper bound, but we have specific constraints for each variable and combinations, so we'll use those directly)
2. $3x_3 - 10x_4 - 7x_5 \geq 0$
3. $13x_1 + 5x_5 \leq 106$
4. $21x_3 + 5x_5 \leq 53$
5. $9x_2 + 10x_4 \leq 80$
6. $9x_2 + 5x_5 \leq 47$
7. $13x_1 + 9x_2 \leq 44$
8. $13x_1 + 21x_3 \leq 95$
9. $9x_2 + 21x_3 \leq 64$
10. $21x_3 + 10x_4 \leq 130$
11. $21x_3 + 10x_4 + 5x_5 \leq 109$
12. $13x_1 + 21x_3 + 10x_4 \leq 101$
13. $13x_1 + 9x_2 + 21x_3 \leq 140$
14. $9x_2 + 10x_4 + 5x_5 \leq 140$
15. $13x_1 + 21x_3 + 5x_5 \leq 118$
16. $13x_1 + 9x_2 + 5x_5 \leq 151$
17. $13x_1 + 9x_2 + 21x_3 + 10x_4 + 5x_5 \leq 151$

## 4: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 5: Implement the objective function and constraints in Gurobi
```python
import gurobi

# Create a new Gurobi model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(lb=0, name="milligrams of potassium")
x2 = model.addVar(lb=0, name="milligrams of vitamin B12")
x3 = model.addVar(lb=0, name="milligrams of calcium")
x4 = model.addVar(lb=0, name="grams of fiber")
x5 = model.addVar(lb=0, name="milligrams of vitamin A")

# Objective function
model.setObjective(6.79*x1 + 6.61*x2 + 3.63*x3 + 6.11*x4 + 2.51*x5, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(3*x3 - 10*x4 - 7*x5 >= 0)
model.addConstr(13*x1 + 5*x5 <= 106)
model.addConstr(21*x3 + 5*x5 <= 53)
model.addConstr(9*x2 + 10*x4 <= 80)
model.addConstr(9*x2 + 5*x5 <= 47)
model.addConstr(13*x1 + 9*x2 <= 44)
model.addConstr(13*x1 + 21*x3 <= 95)
model.addConstr(9*x2 + 21*x3 <= 64)
model.addConstr(21*x3 + 10*x4 <= 130)
model.addConstr(21*x3 + 10*x4 + 5*x5 <= 109)
model.addConstr(13*x1 + 21*x3 + 10*x4 <= 101)
model.addConstr(13*x1 + 9*x2 + 21*x3 <= 140)
model.addConstr(9*x2 + 10*x4 + 5*x5 <= 140)
model.addConstr(13*x1 + 21*x3 + 5*x5 <= 118)
model.addConstr(13*x1 + 9*x2 + 5*x5 <= 151)
model.addConstr(13*x1 + 9*x2 + 21*x3 + 10*x4 + 5*x5 <= 151)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of potassium: ", x1.varValue)
    print("Milligrams of vitamin B12: ", x2.varValue)
    print("Milligrams of calcium: ", x3.varValue)
    print("Grams of fiber: ", x4.varValue)
    print("Milligrams of vitamin A: ", x5.varValue)
    print("Objective function value: ", model.objVal)
else:
    print("No optimal solution found.")
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of potassium'), 
        ('x2', 'milligrams of vitamin B12'), 
        ('x3', 'milligrams of calcium'), 
        ('x4', 'grams of fiber'), 
        ('x5', 'milligrams of vitamin A')
    ], 
    'objective_function': '6.79*x1 + 6.61*x2 + 3.63*x3 + 6.11*x4 + 2.51*x5', 
    'constraints': [
        '3*x3 - 10*x4 - 7*x5 >= 0',
        '13*x1 + 5*x5 <= 106',
        '21*x3 + 5*x5 <= 53',
        '9*x2 + 10*x4 <= 80',
        '9*x2 + 5*x5 <= 47',
        '13*x1 + 9*x2 <= 44',
        '13*x1 + 21*x3 <= 95',
        '9*x2 + 21*x3 <= 64',
        '21*x3 + 10*x4 <= 130',
        '21*x3 + 10*x4 + 5*x5 <= 109',
        '13*x1 + 21*x3 + 10*x4 <= 101',
        '13*x1 + 9*x2 + 21*x3 <= 140',
        '9*x2 + 10*x4 + 5*x5 <= 140',
        '13*x1 + 21*x3 + 5*x5 <= 118',
        '13*x1 + 9*x2 + 5*x5 <= 151',
        '13*x1 + 9*x2 + 21*x3 + 10*x4 + 5*x5 <= 151'
    ]
}
```