## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ = kilograms of silage per cow
- $x_2$ = kilograms of mixed grains per cow

## Step 2: Formulate the objective function
The objective is to minimize the cost of feeding the cows. The cost of silage per kilogram is $215, and the cost of mixed grains per kilogram is $320. Therefore, the objective function to minimize is: $215x_1 + 320x_2$.

## 3: Define the constraints
1. Protein requirement: Each cow requires at least 2 kilograms of protein per day. Since silage contains 0.5 kilograms of protein per kilogram and mixed grains contain 0.2 kilograms of protein per kilogram, we have: $0.5x_1 + 0.2x_2 \geq 2$.
2. Minerals requirement: Each cow requires at least 1.5 kilograms of minerals per day. Since silage contains 0.1 kilograms of minerals per kilogram and mixed grains contain 0.2 kilograms of minerals per kilogram, we have: $0.1x_1 + 0.2x_2 \geq 1.5$.
3. Vitamins limit: Each cow can have at most 1.0 kilograms of vitamins per day. Since silage contains 0.2 kilograms of vitamins per kilogram and mixed grains contain 0.1 kilograms of vitamins per kilogram, we have: $0.2x_1 + 0.1x_2 \leq 1.0$.
4. Non-negativity: $x_1 \geq 0$ and $x_2 \geq 0$.

## 4: Consider the total number of cows
Thomas has 50 cows, but the constraints are per cow. The objective function and constraints formulated are per cow.

## 5: Symbolic representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'kilograms of silage per cow'), ('x2', 'kilograms of mixed grains per cow')],
    'objective_function': '215*x1 + 320*x2',
    'constraints': [
        '0.5*x1 + 0.2*x2 >= 2',
        '0.1*x1 + 0.2*x2 >= 1.5',
        '0.2*x1 + 0.1*x2 <= 1.0',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 6: Gurobi code
Now, let's implement this in Gurobi using Python:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="silage_per_cow", lb=0)
    x2 = model.addVar(name="grains_per_cow", lb=0)

    # Objective function: minimize cost
    model.setObjective(215 * x1 + 320 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(0.5 * x1 + 0.2 * x2 >= 2, name="protein_requirement")
    model.addConstr(0.1 * x1 + 0.2 * x2 >= 1.5, name="minerals_requirement")
    model.addConstr(0.2 * x1 + 0.1 * x2 <= 1.0, name="vitamins_limit")

    # Solve the problem
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Silage per cow: {x1.varValue}")
        print(f"Mixed grains per cow: {x2.varValue}")
        print(f"Total cost per cow: {215 * x1.varValue + 320 * x2.varValue}")
        print(f"Total cost for 50 cows: {(215 * x1.varValue + 320 * x2.varValue) * 50}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```