## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B6' and 'milligrams of potassium'. Let's denote 'milligrams of vitamin B6' as $x_1$ and 'milligrams of potassium' as $x_2$. The objective function to minimize is $1.66x_1^2 + 1.68x_1x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- The kidney support index of $x_1$ is 4, and of $x_2$ is 1. So, $4x_1 + 1x_2 \geq 25$ and $4x_1 + 1x_2 \leq 37$.
- The energy stability index of $x_1$ is 3, and of $x_2$ is 13. So, $3x_1 + 13x_2 \geq 10$ and $3x_1 + 13x_2 \leq 41$.
- $-9x_1 + 5x_2 \geq 0$.
- $x_1$ is an integer, and $x_2$ can be a fraction.

## 3: Write down the symbolic representation
The symbolic representation of the problem is:
- Variables: $x_1$ ('milligrams of vitamin B6'), $x_2$ ('milligrams of potassium')
- Objective function: Minimize $1.66x_1^2 + 1.68x_1x_2$
- Constraints:
  1. $4x_1 + x_2 \geq 25$
  2. $4x_1 + x_2 \leq 37$
  3. $3x_1 + 13x_2 \geq 10$
  4. $3x_1 + 13x_2 \leq 41$
  5. $-9x_1 + 5x_2 \geq 0$
  6. $x_1$ is an integer.

## 4: Convert the problem into Gurobi code
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="milligrams_of_vitamin_B6", vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name="milligrams_of_potassium")

# Set the objective function
model.setObjective(1.66 * x1**2 + 1.68 * x1 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(4 * x1 + x2 >= 25, name="kidney_support_index_min")
model.addConstr(4 * x1 + x2 <= 37, name="kidney_support_index_max")
model.addConstr(3 * x1 + 13 * x2 >= 10, name="energy_stability_index_min")
model.addConstr(3 * x1 + 13 * x2 <= 41, name="energy_stability_index_max")
model.addConstr(-9 * x1 + 5 * x2 >= 0, name="linear_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B6: {x1.varValue}")
    print(f"Milligrams of potassium: {x2.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 5: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of potassium')],
    'objective_function': '1.66*x1^2 + 1.68*x1*x2',
    'constraints': [
        '4*x1 + x2 >= 25',
        '4*x1 + x2 <= 37',
        '3*x1 + 13*x2 >= 10',
        '3*x1 + 13*x2 <= 41',
        '-9*x1 + 5*x2 >= 0'
    ]
}
```