To solve this optimization problem using Gurobi, we first need to clearly define the variables and constraints based on the given information.

Let's denote:
- `x0` as the number of security engineers.
- `x1` as the number of patches per day.

The objective function to minimize is: `9.0*x0 + 2.1*x1`.

Given constraints are:
1. Dollar cost constraint for security engineers and patches per day: `0.63*x0 + 3.53*x1 <= 47`.
2. Available bandwidth impact constraint: `2.84*x0 + 1.26*x1 <= 184`.
3. Network integrity impact constraint: `0.01*x0 + 3.75*x1 <= 183`.
4. Minimum spend on security engineers and patches per day: `0.63*x0 + 3.53*x1 >= 16`.
5. Minimum bandwidth usage: `2.84*x0 + 1.26*x1 >= 36`.
6. Minimum network integrity impact: `0.01*x0 + 3.75*x1 >= 30`.
7. Constraint on security engineers and patches per day relationship: `-2*x0 + x1 >= 0`.
8. Maximum spend constraint: `0.63*x0 + 3.53*x1 <= 43`.
9. Maximum bandwidth usage constraint: `2.84*x0 + 1.26*x1 <= 118`.
10. Maximum network integrity impact: `0.01*x0 + 3.75*x1 <= 105`.

Additionally, we have the constraints that `x0` must be an integer and `x1` cannot be a fraction.

Here's how we can implement this in Gurobi:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="security_engineers")
x1 = m.addVar(vtype=GRB.INTEGER, name="patches_per_day")

# Objective function: Minimize 9.0*x0 + 2.1*x1
m.setObjective(9.0*x0 + 2.1*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(0.63*x0 + 3.53*x1 >= 16, "min_spend")
m.addConstr(0.63*x0 + 3.53*x1 <= 43, "max_spend")
m.addConstr(2.84*x0 + 1.26*x1 >= 36, "min_bandwidth")
m.addConstr(2.84*x0 + 1.26*x1 <= 118, "max_bandwidth")
m.addConstr(0.01*x0 + 3.75*x1 >= 30, "min_integrity")
m.addConstr(0.01*x0 + 3.75*x1 <= 105, "max_integrity")
m.addConstr(-2*x0 + x1 >= 0, "security_patches_relation")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Security Engineers:", x0.x)
    print("Patches per Day:", x1.x)
    print("Objective Function Value:", m.objVal)
else:
    print("No optimal solution found")
```