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

**Decision Variables:**

* `x`: Number of reams of dotted paper produced.
* `y`: Number of reams of grid paper produced.

**Objective Function:**

Maximize profit:  `5.5x + 11y`

**Constraints:**

* Cutting Machine Constraint: `3x + 1.5y <= 3000`
* Printing Machine Constraint: `5.5x + 7y <= 3000`
* Non-negativity Constraints: `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(lb=0, vtype=GRB.CONTINUOUS, name="dotted_paper")
y = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="grid_paper")

# Set objective function
model.setObjective(5.5 * x + 11 * y, GRB.MAXIMIZE)

# Add constraints
model.addConstr(3 * x + 1.5 * y <= 3000, "cutting_constraint")
model.addConstr(5.5 * x + 7 * y <= 3000, "printing_constraint")

# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Dotted Paper (reams): {x.x}")
    print(f"Grid Paper (reams): {y.x}")
    print(f"Maximum Profit: ${model.objVal}")
elif model.status == GRB.INFEASIBLE:
    print("Model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
