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

**Decision Variables:**

*  `x`: Number of reams of lined paper produced.
*  `y`: Number of reams of graph paper produced.

**Objective Function:**

Maximize profit: `11x + 13y`

**Constraints:**

* Cutting machine time: `2x + 2y <= 3500`
* Line-printing machine time: `6x + 10y <= 3500`
* Non-negativity: `x >= 0`, `y >= 0`


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

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

# Create decision variables
x = model.addVar(vtype=GRB.CONTINUOUS, name="lined_paper")
y = model.addVar(vtype=GRB.CONTINUOUS, name="graph_paper")

# Set objective function
model.setObjective(11 * x + 13 * y, GRB.MAXIMIZE)

# Add constraints
model.addConstr(2 * x + 2 * y <= 3500, "cutting_constraint")
model.addConstr(6 * x + 10 * y <= 3500, "printing_constraint")

# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print(f"Optimal profit: ${model.objVal:.2f}")
    print(f"Lined paper: {x.x:.2f} reams")
    print(f"Graph paper: {y.x:.2f} reams")
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {model.status}.")

```
