## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of calcium' and 'milligrams of potassium', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $7x_1 + 5x_2$.

## Step 3: List the constraints
The constraints given are:
- $8x_1 + 4x_2 \geq 18$
- $x_1 + 7x_2 \geq 31$
- $4x_1 + 4x_2 \geq 36$
- $12x_1 + 7x_2 \geq 26$
- $x_1 - 6x_2 \geq 0$
- $8x_1 + 4x_2 \leq 97$
- $x_1 + 7x_2 \leq 120$
- $4x_1 + 4x_2 \leq 62$
- $12x_1 + 7x_2 \leq 102$

## 4: Consider the variable bounds
- $x_1$ can be any real number (non-whole number of milligrams of calcium)
- $x_2$ must be an integer (whole number amount of milligrams of potassium)

## 5: Convert the problem into a Gurobi code
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x1 = m.addVar(name="milligrams_of_calcium", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x2 = m.addVar(name="milligrams_of_potassium", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY, vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(7 * x1 + 5 * x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(8 * x1 + 4 * x2 >= 18, name="kidney_support_index")
m.addConstr(x1 + 7 * x2 >= 31, name="energy_stability_index")
m.addConstr(4 * x1 + 4 * x2 >= 36, name="digestive_support_index")
m.addConstr(12 * x1 + 7 * x2 >= 26, name="cognitive_performance_index")
m.addConstr(x1 - 6 * x2 >= 0, name="calcium_potassium_ratio")
m.addConstr(8 * x1 + 4 * x2 <= 97, name="kidney_support_index_upper_bound")
m.addConstr(x1 + 7 * x2 <= 120, name="energy_stability_index_upper_bound")
m.addConstr(4 * x1 + 4 * x2 <= 62, name="digestive_support_index_upper_bound")
m.addConstr(12 * x1 + 7 * x2 <= 102, name="cognitive_performance_index_upper_bound")

# Optimize the model
m.optimize()

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

## 6: Symbolic representation of the problem
The symbolic representation is as follows:
```json
{
    'sym_variables': [('x1', 'milligrams of calcium'), ('x2', 'milligrams of potassium')],
    'objective_function': '7*x1 + 5*x2',
    'constraints': [
        '8*x1 + 4*x2 >= 18',
        'x1 + 7*x2 >= 31',
        '4*x1 + 4*x2 >= 36',
        '12*x1 + 7*x2 >= 26',
        'x1 - 6*x2 >= 0',
        '8*x1 + 4*x2 <= 97',
        'x1 + 7*x2 <= 120',
        '4*x1 + 4*x2 <= 62',
        '12*x1 + 7*x2 <= 102'
    ]
}
```