Here's the formulation of the problem and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Kilograms of silage purchased per day.
* `y`: Kilograms of mixed grains purchased per day.

**Objective Function:**

Minimize the total cost:

```
Minimize: 215x + 320y
```

**Constraints:**

* **Protein:** 0.5x + 0.2y >= 2 * 50  (Each cow needs 2kg, and there are 50 cows)
* **Vitamins:** 0.2x + 0.1y <= 1 * 50 (Each cow can have at most 1kg, and there are 50 cows)
* **Minerals:** 0.1x + 0.2y >= 1.5 * 50 (Each cow needs 1.5kg, and there are 50 cows)
* **Non-negativity:** x, y >= 0


```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
x = m.addVar(lb=0, name="silage") # Kilograms of silage
y = m.addVar(lb=0, name="grains") # Kilograms of mixed grains

# Set objective function
m.setObjective(215*x + 320*y, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.5*x + 0.2*y >= 100, "Protein")
m.addConstr(0.2*x + 0.1*y <= 50, "Vitamins")
m.addConstr(0.1*x + 0.2*y >= 75, "Minerals")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal cost: ${m.objVal:.2f}")
    print(f"Kilograms of silage: {x.x:.2f}")
    print(f"Kilograms of mixed grains: {y.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
