To convert the given problem into a Gurobi model, we first need to define the decision variables and then formulate the objective function and constraints based on the provided information.

The decision variables are:
- `x0`: hours worked by Peggy
- `x1`: hours worked by Dale
- `x2`: hours worked by John

The objective is to minimize: `1*x0 + 5*x1 + 2*x2`

Constraints are defined based on the organization score, paperwork competence rating, productivity rating, likelihood to quit index, and computer competence rating for each individual, as well as combinations of these ratings. Additionally, there are constraints involving linear combinations of hours worked by different individuals.

Here is how we can implement this in Gurobi:

```python
from gurobipy import *

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

# Define the decision variables
x0 = m.addVar(name="hours_worked_by_Peggy")
x1 = m.addVar(name="hours_worked_by_Dale")
x2 = m.addVar(name="hours_worked_by_John")

# Objective function: minimize 1*x0 + 5*x1 + 2*x2
m.setObjective(1*x0 + 5*x1 + 2*x2, GRB.MINIMIZE)

# Constraints based on the problem description

# Organization score constraints
m.addConstr(20*x0 + 6*x2 >= 71)
m.addConstr(2*x1 + 6*x2 >= 34)
m.addConstr(20*x0 + 2*x1 + 6*x2 >= 34)

# Paperwork competence rating constraints
m.addConstr(9*x0 + 19*x2 >= 105)
m.addConstr(6*x1 + 19*x2 >= 99)
m.addConstr(9*x0 + 6*x1 + 19*x2 >= 99)
m.addConstr(9*x0 + 6*x1 <= 273) # Maximum constraint
m.addConstr(9*x0 + 19*x2 <= 204) # Maximum constraint

# Productivity rating constraints
m.addConstr(13*x0 + 22*x1 >= 56)
m.addConstr(13*x0 + 15*x2 >= 79)
m.addConstr(22*x1 + 15*x2 >= 90)
m.addConstr(13*x0 + 22*x1 + 15*x2 >= 90)
m.addConstr(13*x0 + 22*x1 <= 214) # Maximum constraint
m.addConstr(22*x1 + 15*x2 <= 190) # Maximum constraint
m.addConstr(13*x0 + 22*x1 + 15*x2 <= 211) # Maximum constraint

# Likelihood to quit index constraints
m.addConstr(10*x0 + 14*x1 >= 105)
m.addConstr(10*x0 + 12*x2 >= 109)
m.addConstr(14*x1 + 12*x2 >= 54)
m.addConstr(10*x0 + 14*x1 + 12*x2 >= 88)

# Computer competence rating constraints
m.addConstr(4*x0 + 8*x2 >= 30)
m.addConstr(4*x0 + 12*x1 + 8*x2 >= 30)
m.addConstr(12*x1 + 8*x2 <= 73) # Maximum constraint
m.addConstr(4*x0 + 8*x2 <= 139) # Maximum constraint
m.addConstr(4*x0 + 12*x1 + 8*x2 <= 60) # Maximum constraint

# Additional linear constraints
m.addConstr(7*x0 - 10*x1 >= 0)
m.addConstr(-10*x0 + 5*x2 >= 0)

# Solve the model
m.optimize()

# Print the solution
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Objective:", m.objVal)
```