## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'milligrams of vitamin K', 'milligrams of zinc', and 'milligrams of vitamin B12'. Let's denote these as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $2x_0 + 2x_1 + 5x_2$.

## 3: Define the symbolic variables and their corresponding natural language objects
The symbolic variables and their corresponding objects are:
- $x_0$ : milligrams of vitamin K
- $x_1$ : milligrams of zinc
- $x_2$ : milligrams of vitamin B12

## 4: List the constraints in symbolic notation
The constraints are:
- $6x_0 + 8x_1 + 9x_2 \geq 12$ (total muscle growth index from all)
- $6x_0 + 8x_1 \geq 10$ (total muscle growth index from $x_0$ and $x_1$)
- $6x_0 + 9x_2 \geq 12$ (total muscle growth index from $x_0$ and $x_2$)
- $6x_0 + 3x_1 + 8x_2 \geq 29$ (total kidney support index from all)
- $6x_0 + 3x_1 \geq 25$ (total kidney support index from $x_0$ and $x_1$)
- $6x_0 + 8x_2 \geq 33$ (total kidney support index from $x_0$ and $x_2$)
- $3x_1 + 8x_2 \geq 29$ (total kidney support index from $x_1$ and $x_2$)
- $7x_1 + 9x_2 \geq 12$ (total cognitive performance index from $x_1$ and $x_2$)
- $4x_0 + 9x_2 \geq 10$ (total cognitive performance index from $x_0$ and $x_2$)
- $4x_0 + 7x_1 \geq 13$ (total cognitive performance index from $x_0$ and $x_1$)
- $4x_0 + 7x_1 + 9x_2 \geq 12$ (total cognitive performance index from all)
- $-4x_0 + 2x_1 \geq 0$
- $-9x_0 + 3x_2 \geq 0$
- $4x_0 + 9x_2 \leq 45$ (total cognitive performance index from $x_0$ and $x_2$)
- $4x_0 + 7x_1 \leq 58$ (total cognitive performance index from $x_0$ and $x_1$)

## 5: Specify variable bounds
- $x_0$ is continuous
- $x_1$ is integer
- $x_2$ is integer

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin K'), ('x1', 'milligrams of zinc'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '2*x0 + 2*x1 + 5*x2',
    'constraints': [
        '6*x0 + 8*x1 + 9*x2 >= 12',
        '6*x0 + 8*x1 >= 10',
        '6*x0 + 9*x2 >= 12',
        '6*x0 + 3*x1 + 8*x2 >= 29',
        '6*x0 + 3*x1 >= 25',
        '6*x0 + 8*x2 >= 33',
        '3*x1 + 8*x2 >= 29',
        '7*x1 + 9*x2 >= 12',
        '4*x0 + 9*x2 >= 10',
        '4*x0 + 7*x1 >= 13',
        '4*x0 + 7*x1 + 9*x2 >= 12',
        '-4*x0 + 2*x1 >= 0',
        '-9*x0 + 3*x2 >= 0',
        '4*x0 + 9*x2 <= 45',
        '4*x0 + 7*x1 <= 58'
    ]
}
```

## 7: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x1 = model.addVar(name="x1", lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="x2", lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER)

    # Define objective function
    model.setObjective(2*x0 + 2*x1 + 5*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(6*x0 + 8*x1 + 9*x2 >= 12)
    model.addConstr(6*x0 + 8*x1 >= 10)
    model.addConstr(6*x0 + 9*x2 >= 12)
    model.addConstr(6*x0 + 3*x1 + 8*x2 >= 29)
    model.addConstr(6*x0 + 3*x1 >= 25)
    model.addConstr(6*x0 + 8*x2 >= 33)
    model.addConstr(3*x1 + 8*x2 >= 29)
    model.addConstr(7*x1 + 9*x2 >= 12)
    model.addConstr(4*x0 + 9*x2 >= 10)
    model.addConstr(4*x0 + 7*x1 >= 13)
    model.addConstr(4*x0 + 7*x1 + 9*x2 >= 12)
    model.addConstr(-4*x0 + 2*x1 >= 0)
    model.addConstr(-9*x0 + 3*x2 >= 0)
    model.addConstr(4*x0 + 9*x2 <= 45)
    model.addConstr(4*x0 + 7*x1 <= 58)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x0: {x0.varValue}")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```