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

**Decision Variables:**

* `x`: Number of bottles of sports drink Jason consumes.
* `y`: Number of bottles of coconut water Jason consumes.

**Objective Function:**

Minimize the total cost:  `5x + 3y`

**Constraints:**

* Calcium: `x + 4y >= 10`
* Potassium: `2x + y >= 15`
* Magnesium: `3x + 2y >= 13`
* Non-negativity: `x >= 0`, `y >= 0`


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

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

# Create variables
x = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="sports_drink")
y = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="coconut_water")

# Set objective function
m.setObjective(5*x + 3*y, GRB.MINIMIZE)

# Add constraints
m.addConstr(x + 4*y >= 10, "calcium")
m.addConstr(2*x + y >= 15, "potassium")
m.addConstr(3*x + 2*y >= 13, "magnesium")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal cost: ${m.objVal:.2f}")
    print(f"Sports drinks: {x.x:.2f} bottles")
    print(f"Coconut water: {y.x:.2f} bottles")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
